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HERE - Convergent Science Network (CSN)

1. Figure 4 diagrams the simplest case of two connected neurons It is important to notice which elements belong to which framework Groups effectively only comprise of the neuron soma where all inputs are summed using the function of the specific neuron type see Appendix Neuron types The connection framework deals with axon synapse and dendrite of the neurons The computational element is the synapse which is calculating its own internal state and the signal transmission according to the type see Appendix Il Synapse types Hint The distinction between group and connection is not a biological fact per se but an abstraction within the framework of iqr that allows an easier approach to modeling biological systems 2 2 Conventions used in the manual There are a few conventions that are used trough out this manual Text in a gray box refers to entries in iqr s menu and also to text on dialog boxes th ai m P i e i _ 4 ll Se a le oe Fee en Se ee Se hnapter 2 a iy 2 i n ai Tei Te 7 Te Be E ia m Working with iar Ulysses Bernardet 3 Starting iqr To start iqr enter iqr exe Windows or iqr exe Linux at the prompt in a terminal window or double click on the icon WAF on the desktop The table below lists the command line options igr supports f lt filename gt file lt filename gt open system file lt filename gt c lt filename gt automatically start simul
2. If V exceeds the firing threshold 6 the neuron emits a spike The duration of a spike is 1 simulation time step ts and is followed by a refractory period of 1 ts Pontine nuclei granule cells and inferior olive are modelled with such integrate and fire neurons Purkinje Cell The Purkinje cell is defined by three different compartments PuSp accounts for the tonic spontaneous activity of the Purkinje cell PuSo represents the soma and PuSy represents the dendritic region where synapses with parallel fibres are formed PuSp is spontaneously active as long as it is not inhibited by the inhibitory inter neurons I PuSo receives excitation from PuSp PuSy and JO PuSy represents the postsynaptic responses in Purkinje cell dendrites to parallel fibres stimulation PuSy does not emit spikes but shows continuous dynamics according to Ailt H V t A V t In order for the Purkinje cell to form an association between the CS and the US the model assumes that a prolonged trace of the CS an eligibility trace is present in the dendrites PuSy An exponentially decaying trace with a fixed time constant defines the duration of this eligibility trace for synaptic changes in Purkinje dendrites Deep Nucleus A N neurons are constantly inhibited by Purkinje cell activity unless a pause in PuSo activity occurs Following disinhibition the A N neuron shows a characteristic feature called rebound excitation 21 The output of the A N is the
3. The below the most important parameters of the video module are listed Some parameters are only available under Linux Hint Linux If you encounter problems with the video module run the application xawtv to check whether video capturing works for your computer e Parameters name Video Device Linux Only description Path to Video Device If the computer has more than one capture card or an USB video device is attached in addition to an existing capture card you will have to change to dev video7 or higher default dev videoO name Show output description Show camera output options true false name Image Width Linux Only description Width of the image to grab range options 160 640 name Image Height Linux Only description Height of the image to grab range options 120 480 name HSV description This option allows to choose between the hue value saturation HSV and red green blue RGB color spaces options true false name Monochrome description If set to true one a monochrome image will be acquired The values will be written into the Red Hue group options true false e Output States name Video Output Red Hue Grey direction Module Group range 0 0 1 0 name Video Output Green Saturation direction Module Group range 0 0 1 0 name Video Output Blue Value direction Module Group range 0 0 1 0 Attention The sizes of the three color groups ha
4. and approx 150 columns B the true values are 120 and 160 From the upper C and lower D values of the color bar we see that the maximum value mapped is between 0 9 and 1 0 and the minimum between 0 3 and 0 4 When we use a sensor device the first step is to check the range of its output signal In iqr we can this by setting the output of the sensor module to a neuron group of linear thresholds and then check for the output in the space plot The min and max values in the bar of the space plot will give us an idea of the approximate range 12 Tutorial 4 Classification In this tutorial we will learn to discriminate a group input into two different classes We will apply this operation to real time video in order to obtain a 2 color output The result of this exercise is implemented in the file classification iqr However the file is not complete As an exercise you have to set the missing parameters while reading the tutorial 12 1 Introduction Most information that we can get via sensors is expressed as numerical values in a range For instance the value of pixel in a monochrome image indicates the luminosity level in a region of the image In some cases this may be encoded by an integer number between 0 and 255 thus providing 256 different possible values In the case of the output of our video module the values are encoded as decimal numbers between 0 and 1 and to fulfill your curiosity there are 256 different val
5. blue A more detailed description on how to use the diagram editing toolbar will be given when outlining the editing of the system 4 2 1 Splitting the diagram pane Split the diagram editing pane into two separate views by using the splitter J L Figure 56 From left to right split vertically horizontally revert to single window view 4 3 Navigating the diagram To navigate on a large diagram that does not fit within the diagram editing pane the panner Figure 5 can be used to change to the visible section of the panel 4 4 Browser On the left side of the screen Figure 5 a tree view of the model the browser can be found It provides direct access to the elements of the system The top node of the tree represents the system level the second level node reflects the processes and the third level node points to the groups By double clicking on the system or process node you can open the corresponding diagram in the diagram editing pane Right clicking on any node brings up the context menu 4 5 Copy and Paste elements You can copy and paste a process one or more groups and a connection to the clipboard To copy an object right click on it and select Copy from the context menu To paste the object select Edit Paste from the main menu Processes can only be pasted at the system level groups and connections only at the process level 4 6 Print and save the diagram You can export the diagram as PNG
6. pair 1 3 1 pre 1 1 pose pair 2 8 5 1 pre 2 1 post Note that in the second pair the point in the pre synaptic group is not the location of a neuron iqr provides several ways to specify the list of pairs These different methods are referred to as pattern types The meaning of the pattern types and how to define them is explained in detail below projected point projected point pre synaptic 1 1 2 1 a J gt arborization arborization lt _ ba 1 a a id L zal a i i T SS _ m post synaptic A EA Figure 15 Nexuses pattern and arborization Arborization As previously mentioned the pattern does not relate to neurons directly but to coordinates in the lattice of the groups It is the arborization that defines the real neurons that send and receive input This can be imagined as the arrangement of the dendrites of the post synaptic neurons as depicted in Figure 15 The arborization is applied to every pair of point defined by the pattern Whether the arborization is applied to the pre or the post synaptic group is defined by the direction parameter If applied to the source group the effect is a fan in receptive field A fan out projective field is the result of applying the arborization to the target group see also Figure 13 projected Figure 16 Rectangular arborization iqr provides a set of shapes of arborizations Figure 16 shows an example of
7. 2 TiltFactor where PanFactor Panmax PanminyfirsP TiltFactor Tiltmax TiltminvarsT Publications Cerebellar Memory Transfer and Partial Savings during Motor Learning A Robotic Study Riccardo Zucca Paul F M J Verschure T J Prescott et al Eds Living Machines 2012 LNAI 7375 pp 321 332 2012 Springer Verlag Berlin Heidelberg 2012 1 Introduction A common observation when we learn a new motor skill is that re learning the same activity later in time appears to be faster In the context of Pavlovian classical conditioning of motor responses this phenomenon is known as savings and it has been subject of different studies 1 2 Classical conditioning of discrete motor responses such as the eye blink reflex involves the contiguous presentations of a neutral conditioned stimulus CS e g a tone or a light which normally does not elicit a response with a reflex evoking unconditioned stimulus US e g an air puff towards the cornea that causes the closure of the eyelid 3 The repeated paired presentations of these two stimuli CS and US finally result in the expression of a conditioned response CR that predicts and anticipates the occurrence of the US e g the eyelid closure in response to the tone Non reinforced presentations of a previously reinforced CS induce a progressive elimination of the CR that is known as extinction However even if no more CR is produced a residual of the original lear
8. Artificial Intelligence pp 133 188 International Thomson Computer Press Retrieved from http tinyurl com yatltoo Vico G 1711 De antiquissima Italorum sapientia ex linguae originibus eruenda librir tres On the Most Ancient Wisom of the Italians Unearthed form the Origins of the Latin Language including the Disputation with The Giornale de Letterati D Italia 1711 translate Cornell Paperbacks
9. Neuron properties threshold excitatory gain membrane persistence e Link multiple cell groups of different types using connections e Synapses amp connection properties synapse type uniform fixed weight pattern map e Connection plot 9 2 Introduction In igr models the main difference between types of neurons for mathematical formalization please refer to the iqr manual is the transfer function between the inputs and the outputs and how the inputs are integrated in the membrane potential Linear threshold LT it sums up all the inputs with a certain gain excitatory gain for excitatory connections or inhibitory gain for inhibitory connections Then there is a threshold Once the integration of the inputs what constitutes the membrane potential is over the threshold the output follows linearly the input otherwise output is zero The integration of the inputs can be also manipulated by a parameter called persistence This parameter determines how much of the Membrane Potential in time t still remains in time t 1 This allows a neuron to integrate signals over time not only instantaneously therefore providing some sort of memory Integrate and Fire IF it works very similar to LT neuron but there is a big difference when the membrane potential reaches the threshold the IF neuron spikes with maximum amplitude usually is 1 independently of the input and then resets the membrane potential to 0 Again it
10. Patterns and arborizations 4 18 Creating connections 4 18 1 Connection across processes 4 19 Specifying connections 4 19 1 Pattern 4 19 1 1 PatternForeach 4 19 1 2 PatternMapped 4 19 1 3 Pattern Tuples 4 19 2 Arborization 4 19 3 DelayFunction 4 19 4 AttenuationFunction 4 20 Running the simulation 4 21 Visualizing states and collecting data 4 21 1 State Panel 4 21 2 Drag amp drop 4 21 3 Space plots 4 21 4 Time plots 4 21 5 Connection plots 4 21 6 Data Sampler 4 21 7 Saving and loading configurations 4 22 Manipulating states 4 23 Support for work flow of simulation experiments 4 24 Modules Chapter 3 Writing User defined Types 5 Concepts 5 1 Object model 5 2 Data representation 5 2 1 The StateArray 5 2 2 States in neurons and synapses 5 2 2 1 State related functions in neurons 5 2 2 2 State related functions in synapses 9 2 3 Using history 5 2 4 Modules and access to states 5 2 4 1 Access protection 5 3 Defining parameters 5 3 1 Usage 5 4 Where to store the types 6 Example implementations 6 1 Neurons 6 1 1 Header 6 1 2 Source 6 2 Synapses 6 2 1 Header 6 2 2 Source 6 3 Modules 6 3 1 Header 6 3 2 Source 6 4 Threaded modules 6 5 Module errors Chapter 4 Tutorials 7 Introduction 8 Tutorial 1 Creating a simulation 8 1 Aims 8 2 Advice 8 3 Building the System 8 4 Exercise 9 Tutorial 2 Cell Types Synapses amp Run time State Manipulation 9 1 Aims 9 2 Introduction 9 3 Building the
11. adaptive control The selforganization of structured behavior Robotics and Autonomous Systems 9 181 196 ID 114 ID 3551 1992 12 Verschure P Mintz M A real time model of the cerebellar circuitry underlying classical conditioning A combined simulation and robotics study Neurocomputing 38 40 1019 1024 2001 13 Hofst otter C Mintz M Verschure P The cerebellum in action a simulation and robotics study European Journal of Neuroscience 16 7 1361 1376 2002 14 Ohyama T Nores W Medina J Riusech F A Mauk M Learning induced plasticity in deep cerebellar nucleus The Journal of Neuroscience 26 49 12656 12663 2006 15 Pugh J Raman l Mechanisms of potentiation of mossy fiber EPSCs in the cerebellar nuclei by coincident synaptic excitation and inhibition The Journal of Neuroscience 28 42 10549 10560 2008 16 Zhang W Linden D Long term depression at the mossy fiber deep cerebellar nucleus synapse The Journal of Neuroscience 26 26 6935 6944 2006 17 Bernardet U Verschure P iqr A tool for the construction of multilevel simulations of brain and behaviour Neuroinformatics 8 113 134 2010 doi 10 1007 s12021 010 9069 7 18 Marr D A theory of cerebellar cortex J Neurophysiology 202 437 470 1969 19 Albus J A theory of cerebellar function Math Biosci 10 25 61 1971 20 Hesslow G Ivarsson M Inhibition of the inferior olive during classi
12. and draw the pattern in the drawing area To clear the pattern drawing area press clear or to selectively erase points from pattern press the button You can now add the pattern to the pattern list by clicking on Add or you can replace the selected pattern in the list with your new pattern by pressing Replace Pattern list To manipulate the list of patterns use the toolbar below the list of patterns amp Move the selected pattern up or down in the list l Invert the order of the list i Delete the selected pattern fal Save the list of pattern i Save the list of pattern under a new name i Open an existing list of pattern Sending options As mentioned previously the State Manipulation affects the activity of neurons You can choose from three different modes e Clamp The activity of the neurons is set to the values of the pattern e Add The values from the pattern are added to the activity of the neuron e Multiply The values from the pattern are multiplied to the activity of the neuron In the frame Play Back you can specify how many times the patterns in the list will be applied either select Forever or Times and change the value in the spin box next to it The Interval determines the number of time steps to wait prior to sending the next pattern to the group E g a step size of 1 will send a pattern at each time step step size 2 only at time step 1 3 5 etc StepSize controls which patterns from the l
13. and relations between elements of the system which implies an understanding of the role of the elements and their interactions within the system to achieve its goal Moreover construction entails that we make explicit statements about the abstractions we make Secondly man made devices are open to unlimited manipulation and measurement With respect to manipulation modern investigation techniques allow the manipulation of biological system at different levels and in different domains For example induction of magnetic fields effects permit manipulations at a global level whereas current injection causes local changes to the system Contrary to this synthetic systems can be manipulated at all levels as well as at the local and global scale As an example properties of ion channels can be altered on a single dendrite of a neuron or of all neurons system wide Additionally changes in a synthetic system are always reversible and can be applied and removed as often as required Last but not least manipulation of synthetic systems does not yet have ethical ramifications The development of measurement methods has undergone rapid progress in the past decades Yet the access to internal states of biological systems is still strongly limited especially the simultaneous recording at the detail level over large area Measurement techniques applied to biological systems are often invasive and alter the very system under investigation Synthetic syst
14. for setting the region is the same as explained for PatternForeach If the mapping type is set to center only the central projection points are used whereas if all is selected all cells in the mapped sector are used 4 19 1 3 PatternTuples The Pattern Tuples serves to define individual cell to cell projections You can associate an arbitrary number of pre synaptic with an arbitrary number of post synaptic cells A tuple t as used in this definition is the combination of n source cells with p target cells to preg prep posty post For the set of pre synaptic and post synaptic cells the Cartesian product will be calculated The pattern p itself is the list of tuples p fip a a aig tat An example illustrates the concept We have two tuples tO and t1 to preo prey preg posty ty prea posti posts posts After the Cartesian product the tuples are to prep posto prer posto prea pasta ti 1 pres post pres posts pres posts The pattern p consists of the combination of the two tuples p f preg posto prei posto pres posto prea postih pres posts prea posts gt Property dialog Pattern Patternluples Source i Add Clear 3 8 7 4 lt gt f1 1 3 3 5 6 6 4 lt gt 1 1 2 1 4 3 4 7 8 6 lt gt 2 1 Remove Show Apply Figure 23 Dialog to define pattern Tuples for a connection To d
15. given by uE 1 WmPrsjvj Tl ExcGain x wija t diz j ri I nhGain y Wik klt fik where VmPrs 0 1 is the persistence of the membrane potential ExcGain and InhGain are the gains of the excitatory excln and inhibitory inhIn inputs respectively m is the number of excitatory inputs n is the number of inhibitory inputs wy and wx are the strengths of the synaptic connections between cells and and and k respectively a and a are the output activities of cells j and k respectively and 0 2 0 and x 2 0 are the delays along the connections between cells and j and cells and k respectively The values of w and 6 are set by the synapse type and are described in Appendix Synapse types The output activity of cell at time t 1 a t 1 is given by VAE L u t 1 with probability Prob for v t 1 ThSet io e ama otherwise where hSet is the membrane potential threshold Rand 0 1 is a random number and Prob is the probability of activity e Parameters name Excitatory gain ExcGain description Gain of excitatory inputs The inputs are summed before being multiplied by this gain range 0 0 10 0 name Inhibitory gain InhGain description Gain of inhibitory inputs The inputs are summed before being multiplied by this gain range 0 0 10 0 name Membrane persistence VmPrs description Proportion of the membrane potential remaining after one time step if no in
16. main functions used to deal with states Additional functions for the individual types are listed in the appendix Adding an internal state to neurons and synapses is done via the wrapper class ClsStateVariable ClsStateVariable pStateVariable porate Variable addsStateVartaplet vst demu rier 7 A state variable visible name To manipulate the state we first extract the StateArray where after we can address and change the state as described above StateArray amp sa pStateVariable gt getStateArray Seuk i hal Mice eso The output state is a special state for neurons and synapses For most neurons the output state will be the activity The neuronal output state is used as input to synapses and modules For synapses the output state acts as an input to neurons A neuron or synapse can only have one output state An output state is defined by means of the addOutputState function CIsStateVariable pActivity pActivictcy acdOurcpurcStats act identirier acriyicy name s States created in this framework are accessible in the GUI for graphing and saving 5 2 2 1 State related functions in neurons The base class for the neuron type automatically creates three input states the excitatory inhibitory and modulatory Therefore you do not create any input state when implementing a neuron type To access the existing ones use the following functions which return a reference to a StateArray StateA
17. more LTD at the pfPU is induced These long latencies CRs are due to the low excitability of the A N that just started to undergo potentiation and a strong rebound can not be induced As a result of LTD induction in the following trials the circuit adapts the CRs latencies until most of the USs are prevented When the CSs are no more followed by a US a gradual increase in the CR latency is observed due to the sole effect of LTP By the end of the session the balanced induction of LTD and LTP stabilizes the CRs latencies at a value of 15 20 ts shorter than the ISI In a couple of cases very short latencies responses were elicited because the CS was intercepted while the robot was still performing a turn These short ISIs didn t allow the AIN membrane potential to return at rest therefore inducing a new rebound In some other cases the robot occasionally missed the CS and a UR was then observed The next experiment was designed to test the extinction of the previously acquired CRs In order to avoid the induction of LTD at the pf Pu synapse the US pathway of the circuit was disconnected The extinction performance of the robot is illustrated by the learning curve in Fig 4b The CRs frequency gradually drops during the first two blocks of CSs until no more responses are observed after the fifth block The consecutive presentations of CSs not followed by the US increase the CRs latencies Fig 4e due to the effect of LTP at the pfPu synapse that is no
18. pane toolbar Figure 5 The cursor will change to ie and you can put down the new process by left clicking in the diagram editing pane To abort the action right click into any free space in the diagram editing pane 4 13 Process properties To change the properties of a process either e double click on the process icon in the diagram editing pane e or right click on the process icon or the process name in the browser and select Properties from the context menu Properties Notes Process Name Process Enable Module x Extemal Path Color select Module current type EPuck Module edit set type EPuck Module Apply Close Figure 8 Process properties dialog Using the process properties dialog see Figure 8 you can change the name of the process as it appears on the diagram or you can add notes The remaining properties in this dialog refer to the usage of modules and will be explained in section 4 24 Modules Attention After changes in the property dialog you must click on Apply before closing the dialog otherwise the changes will be lost 4 14 External processes In large scale models it is not uncommon that several subsystems are combined into a larger model and that certain circuits are used in multiple models iqr supports this via the external processes mechanism processes can be exported to a separate file and linked or imported into an existing system If a process is linked in it remains
19. you want to delete by clicking on it and e press delete row Attention The coordinates of the lattice start at 1 1 in the top left corner Topology TopologySpa E a Dialog to define TopologyParse b Visualization of the defined topology Figure 11 Defining the topology of a group 4 17 Connections Groups and connections are described on two different levels At the process level groups and connections are created and are symbolized as rectangles and lines with arrow heads respectively see Figure 12 a From Figure 4 we know that groups are an abstraction of an assembly of neurons and connections are abstractions of an assembly of axon synapse dendrite nexuses In terms of the structure the description at the process level is therefore underspecified Regarding the group we neither know how many neurons it comprises of nor what the topology the arrangement of the neurons is Regarding the connection we don t know from which source neuron information is transmitted to which target neuron The definition is only complete after specifying these parameters Figure 12 b shows one possible complete definition of the group and the connection source target 25 cells 4 cells Figure 12 Levels of description for connections In the framework of iqr the following assumptions concerning connections are made e there is no delay in axons e the computation takes place in synapses e the transmis
20. 7void 18igrcommon ClsSynapseApicalShunt update 1 StateArray amp synIn getInputState gt getStateArray 20 StateArray amp shunt pApicalShunt gt getStateArray Zak StateArray amp psp pPostsynapticPotential gt getStateArray Ze 2B psp 0 synIn 0 shunt 0 24 6 3 Modules 6 3 1 Header Listing 5 shows the header file for a module As for the neurons and the synapses the constructor for the module is only called once during start up of iqr or if the module type is changed The constructor is not called before each start of the simulation During the simulation iqr will call the update function of the module at every simulation cycle During the process of starting the simulation init is called at the end of the simulation cleanup Any opening of files and devices should therefore be put in init and not in the constructor It is crucial to the working of the module that cleanup resets the module to a state in which init can be called safely again cleanup must hence close any files and devices that were opened in init Modules can receive information from group output state This is achieved with a StateVariablePtr as defined on line 21 Listing 5 Module header example 1 ifndef MODULETEST HPP Z2 define MODULE TRS i Hire 3 A include lt Common Item module hpp gt 5 6namespace iqrcommon 7 8 class ClsModuleTest public ClsModule 9 10 PDuUDLLES LI ClsModule
21. 8 at the edge of the source group icon and then on one of the yellow squares at the edge of the icon for the target group To cancel the creation of a connection right click into any free space in the diagram editing pane Adding and removing vertexes To add a new vertex to the connection hold down the ctrl key and click on the connection To remove a vertex from a connection right click on it and select delete from the context menu 4 18 1 Connection across processes Connecting groups from different processes requires two preparatory steps First you need to split the diagram editing pane into two views by clicking on the split vertical J or split horizontal button from diagram editing toolbar Figure 56 Secondly you will use the tab bar Figure 5 to make each separate view of the diagram editing pane display one of the processes containing the groups you want to connect Now you can connect the groups from the two processes just as described above Upon completion you can return to the single window view by clicking on Ll Figure 19 After connecting groups from different processes you will notice a new icon as shown in Figure 19 at the top edge of the diagram This phantom group represents the group in the process currently not visible which the connection targets to or originates from 4 19 Specifying connections Via the context menu or by double clicking on the connection in the diagram you can access th
22. Control DAC framework 11 a computational neuronal model VM model from now on based on the anatomy of the cerebellum has been previously developed to control a behaving robot 12 13 In those studies the authors investigated how plasticity in the cerebellar cortex can be sufficient to acquire and adaptively control the timing of the CR Nevertheless the model was limited to the role of the cerebellar cortex and deliberately did not include other components of the cerebellar circuit that are supposed to be related to classical conditioning i e the role of the deep nucleus Here we propose in the light of different findings on bi directional synaptic plasticity in the deep nucleus 10 14 15 16 an extension of the VM model with the goal of investigating whether synaptic plasticity between the mossy fibre collaterals originating from the Pontine nuclei and the deep nucleus can support partial transfer and consolidation of the memory of the CS US association The plausibility and robustness of the model has been tested on a mobile robot solving an obstacle avoidance task within a circular arena 2 Materials and Methods 2 1 Cerebellar Neuronal Model The cerebellar model used in this study extends the original VM neural model 12 13 and it has been implemented within the IQR neuronal network simulator 17 running on a Linux machine The original model follows the classical Marr Albus idea that learning in the cerebellum is dependen
23. GO OTG 0 20 Zi 22 Zo Vio ud 24iqrcommon ClsModuleTest init 25 open any devices here 26 27 28void 29iqrcommon ClsModuleTest update 30 Anpue from grop 31 StateArray amp clsStateArrayInput 32 inputStateVariablePtr gt getTarget gt getStateArray SS 34 VOULOUE eo aro 35 StateArray amp clsStateArrayOut outputStateVariable gt getStateArray 36 37 for unsigned int 11 0 ii lt clsStateArrayOut getWidth i1 38 clsStaveArrayOur lO aa 22 39 40 41 42void 43iqrcommon ClsModuleTest cleanup 44 elose any devices here 45 6 4 Threaded modules Threaded modules are derived from the ClsThreadModule class as shown in the code fragment in Listing 7 Listing 7 Threaded module header fragment l include lt Common Item threadModule hpp gt 2 3namespace igrcommon 4 class moduleTTest public ClsThreadModule 5 The main difference in comparison with a non threaded module is the protection of the access to the input and output data structures by means of a mutex as shown in Listing 8 On line 3 we lock the mutex then access the data structure and unlock the mutex when done 8 Listing 8 Threaded module update function Llyod 2iqrcommon moduleTTest update 5 qmutexThread gt lock 4 StateArray amp clsStateArrayOut clsStateVariable0 gt getStateArray 5 for unsigned int 11 0 ii lt clsStateArrayOut getWi
24. On the one hand the simulation cannot run faster than a certain maximum speed as given by the complexity of the system and the speed of the computer On the other hand slight variations in the length of the individual update cycles can occur 4 10 Opening an existing system An existing system is opened by selecting the button amp from the main toolbar Figure 5M or via the menu File Open If you open an existing system the current system will be closed 4 11 Saving the system To save the system press the button in the main toolbar Figure 5 or via menu select File Save System To save a system under a new name select menu File Save System As Connection New Group 3 gt No target defined This might make the file unloadable a Figure 7 Warning dialog for saving invalid systems If your system contains inconsistencies e g connections that have no source and or target defined a dialog as shown in Figure 7 will show up Please take this warning seriously The system will be saved to disk but you should fix the problems mentioned in the warning dialog or you might not be able to re open the system 4 12 Creating processes To add a new process to the system activate the system level in the diagram editing pane by clicking on the left most tab in the tab bar Figure 5 or by double clicking on the system node in the browser Figure 5 Thereafter click the Add Process button in the diagram editing
25. Simulator for large scale neural systems an ers m3 el q i adler ae EL a ee BESET aul ae 7 f A C iia 7 oF de i Seem i kia r Ai i i hs i r k gt r y ee Men a a i ee r s i ipin E EnEn ee ich m ok et en TET Ulysses Bernardet Paul Verschure CSN FP7 24 CSN book series igr Simulator for large scale neural systems Paul Verschure Ulysses Bernardet Copyright 2013 SPECS All rights reserved Published by CSN Book Series Series ISSN Pending ae a el eee a a eats A a aa he JN WET EI il lal ea BLS NWetrork Table of Contents Title Page Acknowledgements Chapter 1 Introduction to igr 1 Epistemological background 1 1 The synthetic approach 1 2 Convergent validation 1 2 1 Real world biorobotics 1 2 2 Large scale models 2 What iqr is 2 1 Models in iqr 2 2 Conventions used in the manual Chapter 2 Working with igr 3 Starting igr 4 The User interface 4 1 Diagram editing pane 4 2 Diagram editing toolbar 4 2 1 Splitting the diagram pane 4 3 Navigating the diagram 4 4 Browser 4 5 Copy and Paste elements 4 6 Print and save the diagram 4 7 iqr Settings 4 8 Creating a new system 4 9 System properties 4 10 Opening an existing system 4 11 Saving the system 4 12 Creating processes 4 13 Process properties 4 14 External processes 4 15 Creating groups 4 16 Group properties 4 16 1 Group neuron type 4 16 2 Group topology 4 17 Connections 4 17 1
26. Space plot onto the plot area or one of the State Panels If dropped onto the plot area the group or region will be added if dropped onto a State Panel you have the choice to replace the State Panel under the drop point or add the group region to the plot To remove a group from the Time plot close the State Panel by clicking in the top right corner The State Panels for Time plots are showing which region of a group is plotted by means of a checker board Figure 31 Depending on the selection the entire group or the region will be high lighting in red Time Plot ia T E I I i exedn H ik if i C inhin ma i II 7 modIn pi PAETA vm v act i I AM i F i A i l i J a ya ite i Eoi z0 v axis live data live data Figure 31 igr Time plot You can zoom into the plot by first clicking S Figure 31 and selecting the region of interest by moving the cursor while keeping the mouse button pressed To return to full view click into the plot area To change the scaling of the y axis use the context menu of the axis for detail see section 4 21 3 Space plots 4 21 5 Connection plots Connection plots Figure 32 are used to visualize the static and dynamic properties of connections and synapses The source group is on the left the target group on the right side To create a new Connection plot right click on a con
27. System 9 4 Exercise 10 Tutorial 3 Changing Synapses amp Logging Data 10 1 Aims 10 2 Building the System 10 3 Exercise 11 Introduction 12 Tutorial 4 Classification 12 1 Introduction 12 2 Example Discrimination between classes of spots in an image 12 3 Implementation 12 4 Links to other domains 12 5 Exercises 13 Tutorial 5 Negative Image 13 1 Introduction 13 2 Implementation 13 3 Links to other domains 13 4 Exercises Chapter 5 Appendices 14 Appendix Neuron types 14 1 Random spike 14 2 Linear threshold 14 3 Integrate amp fire 14 4 Sigmoid 15 Appendix Il Synapse types 15 1 Apical shunt 15 2 Fixed weight 15 3 Uniform fixed weight 16 Appendix Ill Modules 16 1 Threading 16 2 Robots 16 2 1 Khepera and e puck 16 2 2 Video 16 2 3 Lego MindStorm 16 3 Serial VISCA Pan Tilt Chapter 6 Publications Cerebellar Memory Transfer and Partial Savings during Motor Learning A Robotic Study 1 Introduction 2 Materials and Methods 2 1 Cerebellar Neuronal Model 2 2 Model Equations 2 3 Simulated Conditioning Experiments 2 4 Robot Associative Learning Experiments 3 Results 3 1 Results of the Simulated Cerebellar Circuit 3 2 Robotic Experiments Results 4 Discussion Paper s References References Acknowledgements We would like to thank the Convergence Science Network of Biomimetics and Biohybrid Systems FP 248986 for supporting the publication of this ebook which represe
28. Test 12 13 Ny OanGl elbinalta oye 14 void update TS void cleanup 16 17 private 18 ClsModuleTest const ClsModuleTest amp 19 20 ee input from group 2 StateVariablePtr inputStateVariablePtr 22 28 orcs ie ecole 24 ClsStateVariable outputStateVariable 25 26 ClsDoubleParameter pParam 2 jee 28 29 30 endif 6 3 2 Source In the implementation of the module Listing 6 we first define the precompile statement 3 4 to identify the module vis a vis iqr As seen previously a parameter is added 8 13 in the constructor Using the function addInputFromGroup which returns a pointer to a StateVariablePtr we define one input from a group and via addOutputToGroup one output to a group In the update function starting on line 28 we access the input state array with getTarget gt getStateArray 31 and the output with getStateArray 35 Listing 6 Module code example l1 include moduleTest hpp 2 SMAKE MODULE DUL INTERFACE LGrConmmon s s CleMocduleTtest 4 test module 1 5 6iqrcommon ClsModuleTest ClsModuleTest 7 ClsModule 8 pParam addDoubleParameter dummy Par0 9 short description LO C207 0 07 11 L 07 3 12 Longer description L3 xParems p 14 LS Sade nue fron group t I inputStateVariablePtr addInputFromGroup _nameIFG0 IFG 0 17 18 j add Cutout to Grote 17 1 outputStateVariable addOutputToGroup _nameOT
29. We have to select the output of the camera to monochrome Process properties tab output checkbox monchrome We will need two groups CameraOutput and dummy We will send to camera output the information we want to process and to dummy the one we discard To see that check the properties of the the process camera in the tab module to groups See that the Grayscale output is sent to CameraOutput Groups The iqr implementation of this tutorial is quite minimalistic as said above we need two groups but only one of them CameraOutput is involved in the classification Figure 52 Both groups size has to match the camera resolution 160x120 This is set in the group properties section topology for the topologyRect press the edit button the values 160 and 120 Whenever we refer to a groups size we refer to this width and height For the group dummy we do not care of anything else The group cameraOutput does the job First of all recall that we want to give only two kind of outputs O and 1 The neuron type Integrate and Fire does it when it spikes it outputs 1 When it is not spiking it outputs 0 Therefore all we have to do is set the appropiate properties persistence and probability unless otherwise stated is safe to set these values to 0 and 1 in most of the cases this holds for Integrate and Fire Linear threshold and Sigmoid neurons membrane potential rese
30. a rectangular arborization of a width and height of 2 The various types of arborizations are described in detail below Combining pattern and arborization The combination of pattern and arborization is illustrated in Figure 17 The source group is on the left side the target group on the right side In the lower part you find the pattern denoted by a gray arrow The pattern consists of four pairs of points On the upper left tier the arborization is applied to the points defined by the pattern For each cell that lies within the arborization one synapse is created In the case presented in Figure 17 there are 16 synapses created For each synapse the distance with respect to the pattern point is calculated This distance serves as basis to calculate the delay Several functions are available for details see the DelayFunction section further below arborization pattern source group target group information Figure 17 Combining pattern and arborization 4 18 Creating connections To add a new connection click on the corresponding button in the diagram editing pane toolbar Figure 5 Ly red arrow will create an Excitatory L green arrow a Modulatory and blue arrow an Inhibitory connection Group f a l I Figure 18 After clicking on one of the buttons to create a connection you will notice the cursor changing to To position the connection first click on one of the yellow squares Figure 1
31. a separate file which can be worked with independently of system it is integrated in es Ox File Edt Wew Jiagram Dato Help Name 7 SSGOsEr olmal eae System 1 Process 3 Fl ext Process F Carneer ons Process 1 Process 3 h ext Process File Edil iew Dieyran Sala Eep f a gt Yare S S MBE fy Ly cs Ls Sysem 2 ext Process connections ext Process Figure 9 The concept of an external process two igr systems are including the same process which is stored in a separate file Exporting a process A process can be exported from an existing iqr system by right clicking on the process icon in the diagram editing pane or the process name in the browser and choosing the menu entry Export Process By default processes exported by iqr have the extension iqrProcess Importing and linking in a process A process stored in a separate file can be either imported or linked into an existing system In the former case the process is copied into the existing system making the process stored in a separate file obsolete In the case of linking in a process the definition of the process remains in a separate file and is updated every time the system is saved This also means that two or more iqr systems can include exactly the same process Importing and linking are done via the menu File Import Process and File Link in Process In the case of a linked in process t
32. al shunt e States name Postsynaptic potential direction output description e Feedback name Apical dendrite backpropogation description name Apical dendrite shunt description 15 2 Fixed weight e Parameters name Weight description Uniform synaptic weight range options 1 0 1 0 e States name Postsynaptic potential direction output description 15 3 Uniform fixed weight e Parameters name Weight description Uniform weight for all synapses range options 1 0 1 0 e States name Postsynaptic potential direction output description 16 Appendix lll Modules In this appendix the various modules their application and parameters are presented The list below gives an overview of the standard modules that come with iar Additional modules can be found at http sourceforge net projects igr extension 16 1 Threading some of the modules are running in their own thread This means that their update speed is independent of the update speed of the main simulation All modules relating to robots are threaded whereas video modules are not threaded In some cases a low update speed of the video hardware slows down the entire simulation Therefore make sure to employ all means like compression to get a good update speed for the hardware 16 2 Robots 16 2 1 Khepera and e puck Khepera Operating system Linux e puck Operating system Linux Windows This chapter describes the iqr interfaces
33. al values The Data Sampler has the following options e Sampling Defines at what frequency data is saved e Every x Cycle Specifies how often data is saved in relation to the updates of the model e g a value of 1 means data is saved at every update cycle of the model a value of 10 that the data is saved only every 10th cycle e Acquisition These options define for how long data is saved e Continuous Data is saved until you click on Stop e Steps Data is saved for as many update cycles as you define e Target e Save to Name of the file where the data is saved e Overwrite Overwrite the file at every start of sampling e Append Append data to the file at every start of sampling e Sequence Create a new file at every start of sampling The name of the new file is composed of the base name from e Save to with a counter appended e Misc e auto start stop Automatically start and stop sampling when the simulation is started and stopped If the Sampling Acquisition and Target options are set and one or more groups regions of cells are added you can start sampling data To do this click on the Sample button While data is being sampled you will see the lights next to the Sample button blinking To stop sampling click on the Stop button Attention You will only be able to start sampling data if you specified the Sampling Acquisition and Target options and one or more groups were added 4 21 7 Saving and loading c
34. all it Tutorial 3 and start with it Rename also the process e Delete the Sigmoid neuron group e Set the Linear Threshold group properties e Threshold 0 e Membrane Persistence 0 e Change the size of the Linear Threshold group using the Topology option to 30 by 30 neurons if the computer goes slow reduce it to 10x10 for example e Change the size of the Random Spike group to 1 single neuron Save the simulation when you have finished Data Sampler Sampling E y E gt z Ei Every x Cycle Linear Threshold Gaussian Arb Integrate and Mre Linear Threshold Rect Arb states states states Acquisition excin ecin excin Continuous inhin inhin inhin modin modin madin vm vm w act wW act Overwrite 2 ee u Append Seq uence a auto start sbhop average average yw live data w live data Figure 47 Data sampler 10 3 Exercise e Start the simulation and bring up the Space Plot and observe the activity in the Linear Threshold group e We want to modify the connection parameters to generate a rectangular circular and elliptic activation of the post synaptic neuron group For that modify the arborization properties Projective Field and the attenuation In the circular and elliptic case when using attenuation you should get space plots similar to Figure 48 It may be a good idea to check the connection plot to make sure the connection scheme is well set Q1 Write values in the table Space Plo
35. ar Threshold group to have a receptive field that respond to surrounding excitation This means that every post synaptic neuron must integrate the activity of more than one neuron of the pre synaptic group Change the Linear Threshold group dimensions to 1 neuron and the RandomSpike group to 10 by 10 e Change also the connection settings to achieve the correct receptive field Q1 Which are the new parameters Attenuation Synapse ae e Use the state manipulation panel on the RandomSpike group to generate a circular rectangular and gaussian like activation Bring up the Time Plot of the Linear Threshold group Q1 When is it responding maximally e Open the Data Sampler under the Data menu Save some data from the cell groups of your choice Open the data file in OpenOffice or Excel Q1 What you see in the file 11 Introduction For the next tutorials we will use the WEBCAM SPC220NC and the igr video module In general we can consider a camera as a matrix of sensors each pixel gives the reading of a particular sensor in terms of amount of light If we use a 160x120 camera output we are dealing with almost 20 thousand individual sensors La Video Disjus Gn a E CameraQutput states O excin O inhin modin O vm W live data Figure 51 Output of the camera seen as a space plot of the group CameraOutput The group has approx 100 rows A
36. aramlID and the value to be set Items can be addressed either by their name or their ID In the case of name all items with this name are changed This feature can be used to change multiple elements at the same time Documentation of the system The documentation of a system comprises the descriptions of its static and dynamic properties To document the structure of the model iqr allows to export circuit diagrams in the svg and png image format or to print them directly A second avenue of documenting the system is based on the Extensible Markup Language XML http www w3 org XML format of the system files in igr XML formatted text can be transformed into other text formats using the Extensible Stylesheet Language Family XSL One example of such an application is the transformation of a system file into the dot language for drawing directed graphs as hierarchies http Awww graphviz org Another transform is the creation of system descriptions in the LATEX typesetting system 4 24 Modules A Module in iqr is a plug in to exchange data with an external entity This entity can be a sensor camera microphone etc an actor robot motor or an algorithm e Modules read and write the activity of groups e A process can have only one single module associated to it e A module reads from and writes to groups that belong to the process associated with the module To assign a module to a group open the process properties dialog se
37. ase of use the parameter values can be extracted from parameter objects 52 55 On line 58 60 we update the internal state and the output state 64 65 The calculation of the output state may seem strange but becomes clearer when taking into account that StateArray 0 returns a valarray The operation performed here is referred to as subset masking 1 Listing 2 Neuron code example l include neuronIntegrateFire hpp 2 3 5 Interface for dynamice loading is built using a macro AMARE T NEURON Dili INTERFACE 1gr common CTSNEUronIntegrateRire 5 Integrate amp fire 6 7Jigqrcommon ClsNeuronIntegrateFire ClsNeuronIntegrateFire 8 CIS Neineonnaie 9 pVmembrane 0 LO pActivictcy 0 4 11 12 pExcGain addDoubleParameter excGain Excitatory gain 13 Or O20 1050 4 14 Gain of excitatory inputs n LS The inputs are summed before n L6 being multiplied by this gain L7 OIL ONE p 18 19 pInhGain addDoubleParameter inhGain Inhibitory gain 20 IPO OTO m 105074 2i Gain of inhibitory inputs n 22 The inputs are summed before n Z3 being multiplied by this gain 24 SI Mowe 25 26 Membrane persistence 21 pVmPrs addDoubleParameter vmPrs Membrane persistence 28 On Or O07 OA 29 Proportion of the membrane potential n 30 which remains after one time step n 31 if no input arrives 32 Membrane oS 34 pProbability addDoubleParameter probab
38. ation version show version The arguments f lt filename gt c lt filename gt and r can be combined arbitrarily but r only works in combination with f lt filename gt 4 The User interface 4 1 Diagram editing pane The main diagram editing pane Figure 5 serves to add processes groups and connections to the model The definition of a new process automatically adds a new tab and therein a new diagram To switch between diagram panes use the tab bar Figure 5 The left most tab always presents the system level File Edit iew Ciagramn Data Help oa 7 5 Pone Mame z E new svstem I Cum clio s a a ee ee o Yew Prac CProLp I F cpl I Grp 2 INPUS soup New Process 2 Proce ss Figure 5 igr graphical user interface On the diagram editing pane a gray square represents a process a white square a group and a line with an arrow head a connection A single process or group is selected by clicking on its icon in the diagram editing pane To select multiple processes or groups hold down the control key while clicking on the icon 4 2 Diagram editing toolbar The functionality of the diagram editing toolbar Figure 5 is as follows Zoom in and out of the diagram iF Add a new process to the system level i Add a new group to the current process Ls L L Add a new connection between groups excitatory red modulatory green inhibitory
39. ation is depending on the size of the arborization i e height width or outer height outer width respectively The types of attenuation functions are the same as for the delay see above Key difference between the delay and the attenuation function is that in the former case the values are continuous whereas in the latter case they are discrete Figure 25 Attenutation function Linear min 9 max 1 Attenutation function Gaussian min 0 max 1 distmax distmmax distmax disthuiax distmax Distance a Linear Function Atten utation 1 0 8 0 6 sigma 0 5 sigma 0 5 mmm 0 4 Attenutation sigma 1 2 9 a d sigma 3 Distance b Gaussian Function Attenutation function Block min 0 max 1 width 4 1 0 8 0 6 0 4 0 2 Distance c Block Function Figure 26 Attenuation functions 4 20 Running the simulation To start the simulation click on the play button in the main toolbar Figure 50 Attention In the process of starting the simulation iqr will check the model s consistency If the model is not valid e g because a connection target was not defined a warning will pop up You will not be able to start the simulation unless the system is consistent CPS 73 00 Figure 27 The update speed of the simulation is indicated as Cycles Per Second While the simulation is running the update speed in C ycles P er S econd is in
40. atory input We solve this using the Bias group It consists of a single random spike neuron connected to the group Negative with an excitatory connection There are two important points e We don t want activity of the Bias group to be random we want it to be constantly on We achieve this by setting the spike probability to 1 e We have 1 neuron in the Bias group and 19800 neurons 160 x 120 in the Negative group Then this only neuron has to send output to all neurons in the target group meaning that there has to be a connection between the one neuron in Bias and all the neurons in Negative If this is the case then each time the source neuron generates an output as 1 all the neurons in Negative receive this 1 At the level of IQR we can generate this kind of connectivity in two different ways e Set the pattern mapping to ForEach e Set the arborization type to ArbAll and its parameter direction to PF The first configuration means that we generate a connection between all possible neuron couples where a couple contains one neuron from the source and one from the target group For the second configuration you can check what it does Tutorial 2 Warning Connecting two groups of 160 x 120 means creating almost 20 thousand individual links or synapses These are a lot connections and you could notice it by seeing that now iqr needs a bit more time several seconds to start running the simulation You ca
41. cal conditioned response in the decerebrate ferret Exp Brain Res 110 36 46 1996 21 Aizenman C Linden D Rapid synaptically driven increases in the intrinsic excitability of cerebellar deep nuclear neurons Nature Neuroscience 3 2 109 111 2000 22 Ito M Cerebellar long term depression characterization signal transduction and functional roles Physiological Reviews 81 3 1143 1195 2001 23 Racine R Wilson D A Gingell R Sunderland D Long term potentiation in the interpositus and vestibular nuclei in the rat Experimental Brain Research 63 1 158 162 1986 24 Medina J Garcia K Mauk M A mechanism for savings in the cerebellum The Journal of Neuroscience the Official Journal of the Society for Neuroscience 21 11 4081 4089 2001 25 Weidemann G Kehoe E Savings in classical conditioning in the rabbit as a function of extended extinction Learning amp Behavior 31 49 68 2003 26 Hansel C Linden D DAngelo E Beyond parallel fiber Itd the diversity of synaptic and non synaptic plasticity in the cerebellum Nature Neuroscience 4 467 475 2001 27 Kenyon G A model of long term memory storage in the cerebellar cortex a possible role for plasticity at parallel fiber synapses onto stellate basket interneurons PNAS 94 14200 14205 1998 28 Porrill J Dean P Cerebellar motor learning when is cortical plasticity not enough PLoS Computational Biol
42. ceive input from pf and inhibit PuSp Excitatory connections are represented by filled Squares inhibitory connections by filled circles and sites of plasticity by surrounded squares The main modification to the original model is based on the assumption that long term potentiation LTP and long term depression LTD can be also induced in the synapses between the mossy fibres collaterals and the deep nucleus by release of Purkinje cell 2 2 Model Equations As in the original model the network elements are based on a generic type of integrate and fire neurons The dynamics of the membrane potential of neuron at time f 1 V t 1 is given by Vilt 1 8 Vi t E t L t where B 0 1 is the persistence of the membrane potential defining the speed of decay towards the resting state E t represents the summed excitatory and t the summed inhibitory input E t and t are defined as E t p A wy t kit 4 Yws E where is the excitatory and _ the inhibitory gain of the input N is the number of afferent projections A t is the activity of the presynaptic neuron j N at time t and wj the efficacy of the connection between the presynaptic neuron j and the postsynaptic neuron The activity of an integrate and fire neuron at time tis given by A t H V t 6 where 6 is the firing threshold and H is the Heavyside function H x l fa gt 0 H x 0 otherwise
43. ces to hardware Every simulation tool is implicitly or explicitly driven by assumptions about the best approach to understand the system in question The heuristic approach behind iqr is twofold Firstly we have to look at large scale neuronal systems with the connectivity between neurons being more important than detailed models of neurons themselves Secondly simulated systems must be able to interact with the real world iqr therefore provides sophisticated methods to e define connectivity e to perform high speed simulations of large neuronal systems that allow the control real world devices robots in the broader sense in real time and e to interface to a broad range of hardware devices Simulations in igr are cycle based i e all elements of the model are updated in a pseudo parallel way independent of their activity The computation is based on difference equation aS opposed to the approximation of differential equations The neuron model used is a point neuron with no compartments As a simulation tool iqr fits in between high level general purpose simulation tools such as Matlab http www mathworks com includes_content domainRedirect country_select html uri index html amp domain mathworks com and highly specific low level neuronal systems simulators such as NEURON http www neuron yale edu The advantages over a general purpose tool are on the one hand the predefined building blocks that allow easy a
44. ctions The value of the parameter object can be retrieved at run time by virtue of the getValue function Examples for the usage are given in sections 6 7 Neurons 6 2 Synapses and 6 3 Modules The extensive list of these functions is provided in the documentation for the Clsltem class in section 3 1 5 4 Where to store the types The location where iqr loads types from is defined in the iqr settings NeuronPath SynapsePath and ModulePath see igr Manual Best practice is to enable Use user defined nnn where nnn stands for Neuron Synapse or Module and to store the files in the location indicated by the Path to user defined nnn As the neurons synapses and modules are read from disk when iqr starts up any changes to the type while iqr is running has no effect you will have to restart iqr if you make changes to the types 6 Example implementations 6 7 Neurons 6 1 1 Header Let us first have a look at the header file for a specific neuron type As you can see in Listing 1 the only functions that must be reimplemented are the constructor 11 and update 13 Hiding the copy constructor 17 is an optional safety measure Lines 20 21 declare pointers to parameter objects Line 24 declares the two states of the neuron Listing 1 Neuron header example l ifndef NEURONINTEGRATEFIRE HPP 2 0efine NEURONINTEGRATEFIRE HPP 3 A include lt Common Item neuron hpp gt 5 6namespace igqrcommon 7 8 class ClsNeur
45. ctitating membrane classical conditioning and extinction in the albino rabbit Science 138 3536 33 1962 4 Hesslow G Yeo C The functional anatomy of skeletal conditioning In A Neuroscientist s Guide to Classical Conditioning pp 88 146 Springer New York 2002 5 Thompson R Steinmetz J The role of the cerebellum in classical conditioning of discrete behavioral responses Neuroscience 162 3 732 755 2009 6 Miles F Lisberger S Plasticity in the vestibulo ocular reflex a new hypothesis Annual Review of Neuroscience 4 1 273 299 1981 7 Mauk M Donegan N A model of Pavlovian eyelid conditioning based on the synaptic organization of the cerebellum Learning amp Memory 4 1 130 158 1997 8 Perrett S Ruiz B Mauk M Cerebellar cortex lesions disrupt learningdependent timing of conditioned eyelid responses The Journal of Neuroscience 13 4 1708 1718 1993 9 Bao S Chen L Kim J Thompson R Cerebellar cortical inhibition and classical eyeblink conditioning Proceedings of the National Academy of Sciences of the United States of America 99 3 1592 1597 2002 10 Ohyama T Nores W Mauk M Stimulus generalization of conditioned eyelid responses produced without cerebellar cortex implications for plasticity in the cerebellar nuclei Learning amp Memory Cold Spring Harbor N Y 10 5 346 354 2003 11 Verschure P Kr ose B Pfeifer R Distributed
46. cuit to a simulated E puck robot performing an unsupervised obstacle avoidance task in a circular arena Fig 2 During the experiments the occurrences of the CSs and the USs were not controlled by the experimenter instead the stimuli occurred simply as a result of the interaction of the device with the environment The robot is equipped with a set of URs triggered by stimulating the proximity sensors Fig 2 right The activation of the four frontal sensors due to a collision with the wall of the arena US triggers a counter clockwise turn A camera 16x16 pixels constitutes the distal sensor and a group of cells in a different process responds to the detection of a red painted area around the border of the arena signalling the CS Visual CSs and collision USs are conveyed to the respective pathways of the cerebellar circuit resulting in an activation of A 1 for 1 ts The activation of the CR pathway triggers a specific motor CR i e a deceleration and a clockwise turn Camera Proximity _ ore sensors Fig 2 Robot simulation environment Left the robot is placed in a circular arena where a red patch acting as a CS stimulus allows to predict the occurrence of the wall US stimulus Right top view of the E puck sensor model During the experiment the robot is placed in a circular arena exploring its environment at a constant speed Thus in the UR driven behaviour collisions with the wall of the arena occur regularly T
47. d at installation time e Path to local The folder where system wide non standard types are stored e Path to user defined The folder into which the user stores her his own types 4 8 Creating a new system A new system is created by selecting from the main toolbar Figure 5M or via the menu File New System Creating a new system will close any open systems 4 9 System properties To change the properties of the system right click on the system name in the browser top most node and select Properties from the context menu or via the menu File gt System Properties The options for NeuronPath SynapsePath ModulePath refer to the location where the specific files can be found As they follow the same logic the description below applies to all three Properties Notes System Name example Author ulysses Date Poo9 Hostname SyncPlots Port 54923 Cycles Per Second 0 OK Cancel Figure 6 System properties dialog Using the system properties dialog Figure 6 you can change the name of the system the author add a date and notes Hostname has no meaning in the present release The most important property is Cycles Per Second with which you can define the speed at which the simulation is updated A value of 0 means the simulation is running as fast as possible values larger than zero denotes how many updates per cycle are executed The value entered reflects best effort in two ways
48. description Turn Lateral LED on options true false e Control e Parameters name Max Speed MaxSpeed description Maximum speed for the robot range 0 0 1000 name Type of Motor Map description See below and Figure 56a and Figure 56b options VectorMotorMap or TabularMotorMap name Speed Controller Proportional description Set Speed Controller Proportional Value range 0 3800 name Speed Controller Integral description Set Speed Controller Integral Value range 0 3800 name Speed Controller Differential description Set Speed Controller Differential Value range 0 3800 e States name Proximal Sensors Output direction Module Group description size 9 range 0 0 1 0 name Ambient Sensors Output direction Module Group description size 9 range 0 0 1 0 name Position Monitor Output direction Module Group description Two cells that can be used to monitor the x and y position of the robot size 2 range 0 0 1 0 name Motor Input direction Group Module description Motor command for the robot size 9 9 for TabularMotorMap 2 2 for VectorMap range 0 0 1 0 Tabular vs Vector Motor Map There are two ways to control the motors of the robot In Tabular motor map the cell with the highest activity in the Motor Input group is taken and from the position of this cell in the lattice of the group the movement of the robot is computed Figure 56 a shows the Tabular m
49. dicated in the lower left corner of the application window as shown in Figure 27 To stop the simulation click on the play button W again 4 21 Visualizing states and collecting data This section introduces the facilities igqr provides to visualize and save the states of elements of the model Time plots and Group plots are used to visualize the states of neurons Connection plots serve to visualize the connectivity and the states of synapses of a connection The Data Sampler allows to save data from the model to a file Q New Group 1 states excIn 4 AY inhin ei CO m n ho aci a w live data Figure 28 4 21 1 State Panel iqr plots and the Data Sampler share part of their handling Therefore we will first have a look at some common features On the right side of the plots and the Data Sampler you find a State Panel which looks similar to Figure 28 shows the name of the object from which the data originates Neurons and synapses have a number of internal states that vary from type to type In the frame entitled states you can select which state should be plotted or saved Squares in front of the names indicate that several states can be selected simultaneously circles mean that only one single state can be selected Attention In a newly created plot or Data Sampler the check box live data will not be checked which means that no data from this State Panel is plotted or saved To activat
50. dth i1 6 clsStateArrayOut 0 i1 11 i 8 qmutexThread gt unlock 9 LO 6 5 Module errors To have a standardized way of coping with errors occurring in modules the ModuleError class is used Listing 9 shows a possible application of the error class for checking the size of the input and output states Listing 9 Throwing a ModuleError code example Tyoidl 2iqrcommon ClsModuleTest init 3 if inputStateVariablePtr gt getTarget gt getStateArray getWidth 9 4 throw ModuleError string Module 5 Label 6 needs 9 cells for output 7 8 9 if outputStateVariable gt getStateArray getWidth 10 10 throw ModuleError string Module LI label 12 needs 10 cells for input 13 F a i m R lia Te a Chapter 4 im kie Tutorials Ivan Herreros Jose Maria Blanco Calvo Miguel Lechon Ulysses Bernardet 7 Introduction These tutorials will provide you with a practical introduction to using the neural simulation software and give you an insight into the principles of connectionist modeling They are intended to be complementary to the detailed operation manual Detailed instructions on which buttons to press are not included here in most cases 8 Tutorial 1 Creating a simulation 8 7 Aims e Understand and assimilate the basic principles and concepts of iar System Process Group amp its properties neuron type
51. e the State Panel check the tic by clicking the check box To hide the panel with the states of the neuron or synapse drag the bar to the right side 4 21 2 Drag amp drop The visualizing and data collecting facilities of iqr make extensive use of drag amp drop You can drag groups from the Browser Figure 5 the symbol in the top left corner of State Panels Figure 29 or regions from Space plots 5 New Grou state Figure 29 To drag click on any of the above mentioned items and move the mouse while keeping the button pressed The cursor will change to k Move the mouse over the target and drop by releasing the mouse button The table below summarized what can be dragged where Browser Time plot Group plot Data Sampler Group Time plot Group plot Data Sampler Group Data Sampler Time plot Group plot Group Group plot Time plot Data Sampler Group Region 4 21 3 Space plots A Space plot as shown in Figure 30 displays the state of each cell in a group in the plot area To create a new Space plot right click on a group in the diagram editing pane or the browser and select Space plot from the context menu The value for the selected state see above is color coded the range indicated in the color bar A Space plot solely plots one state of one group Nev Group 1 states 3 ecin 5 inhtin 3 miodIn e act live data Figure 30 igr Space plot To change the mode o
52. e mapping of input to output states as in psychophysics and the abstraction of symbol manipulation Figure 1 The levels of organization of the brain range from neuronal sub structures to circuits to brain areas These different levels of abstraction are not mutually exclusive but must be combined into a multi level description Focusing on a single level of abstraction can fall short where only a holistic systemic view can adequately explain the system under investigation One such example can be found in the phenomenon of behavioural feedback which indicates that behaviour itself can induce neuronal organization 1 1 The synthetic approach We argue that an essential tool in our arsenal of methods to advance our understanding of the brain is the construction of artificial brain like systems The maxim of the synthetic approach is truth and the made are convertible put forward by the 17th century philosopher Giambattista Vico The apparent argument is that the structure and the parameters of man made synthetic product are fostering our understanding of the modelled system Yet Vico s proposition brings about two other important aspects Firstly it is the process of building as such that is yielding new insights in building we are compelled to explicitly state the target function of the system and herein possibly err Since we build all elements of the system to fulfil certain functions we explicitely assign meaning to all elements
53. e properties dialog for a connection Attention Click on the connection line not the arrowhead In the properties dialog you can change the name and the notes for a connection as well as the connection type To select and edit the synapse type the same procedure applies as when setting and editing the neuron type for the group section 4 76 7 Group neuron type 4 19 1 Pattern This section explains the meaning of the different types of patterns and how to define them in iqr 4 19 1 1 PatternForeach Figure 20 This pattern type defines a full connectivity where each cell from one group receives information from all the cells of the other group as shown in Figure 20 You can select all cells limit the selection to a region or define a list of cells Property dialog Figure 21 shows the dialog to set the properties for the PatternForeach To define which cells from the source and target group are included use the pull down menu and select from All Region or List Pattern PatternForeach Target Region i Set Clear i Set Clear Selection Start w ii 1 2 01 4 01 5 01 6 01 7 2 7 03 Start Y ra i 4 3 5 3 6 pay 4 4 4 7 5 4 Width eo 1 5 7 6 4 6 RB 6 6 7 6 8 6 9 7 7 8 4 575 8 6 8 7 9 Height 1 4 Apply Clase i Figure 21 To define a list of cells click on each cell you want to add to the list Clicking Set will define the list and the coordinates of the se
54. efine a tuple select a number of pre synaptic source cells and a number of post synaptic cells by clicking on them in the respective drawing area To add the tuple to the list click on Add To clear the drawing area click on Clear The list of tuples is shown in You can remove a tuple by selecting it in and pressing Remove If you select a tuple and click on Show its corresponding cells will be shown in the drawing area Now you can add new cells and save the tuple as a new entry by clicking on Add again 4 19 2 Arborization In Figure 24 all available arborization types bar ArbAl are diagrammed Where applicable the red cross indicates the projection point as defined by the pattern The options available in the property dialogs for the different arborization types are derived from the lengths shown in the figure In addition all types of arborizations have the parameters Direction and Initialization Probability c Eliptic arbonzation 6 Rectangular arborization outer width inner width id Rectangular with window outer width inner width e Eliptic with window Figure 24 Arborization types Property dialog The direction parameter defines which population the arborization is applied to in the case of RF it is applied to the source group in case of PF to the target group With the initialization probability you can set the probability with which a synapse defined by the arbo
55. ems in comparison provide unlimited access to all internal states of the system and therefore pave the way to a much deeper understanding of their internal workings 1 2 Convergent validation The synthetic approach as laid out by Giambattista Vico is a necessary but not sufficient basis for the development of meaningful models Modelling a system can be compared to fitting a line through a number of points the smaller the number of points the more under constrained the fit is Applied to modelling this means that for a small number of constraints the number of models that produce the same result is very large Hence for the synthetic approach to be useful in biological sciences we need to apply as many constraints as possible Constraint are applicable at two levels the level of the construction of the model and at the level of the validation of the behaviour of the model For a large number of constraints as for fitting a line though a large number of points it is difficult to find a good fit initially For this reason the convergent validation method defines modelling as an iterative process where the steps of validating the model against a set of behavioural environmental and neurobiological constraints and adapting the model to be compliant with theses constraints are repeated until a satisfactory result is achieved Figure 2 build construction on model constraints constraints N validate validation m
56. en used in neural networks The membrane potential of a sigmoid cell at time t 1 v t 1 is given by the following equation The output activity a t 1 is given by a t 1 0 5 1 tanh 2 Slope v 1 ThSet where Slope is the slope and ThSet is the midpoint of the sigmoid function respectively e Parameters name Clip potential Clip description Limits the membrane potential to values between VmMax and VmMin Parameters maximum potential VmMax minimum potential VmMin options true false name Excitatory gain ExcGain description Gain of excitatory inputs The inputs are summed before being multiplied by this gain range 0 0 10 0 name Inhibitory gain InhGain description Gain of inhibitory inputs The inputs are summed before being multiplied by this gain range 0 0 10 0 name Membrane persistence VmPrs description Proportion of the membrane potential remaining after one time step if no input arrives range 0 0 1 0 name Minimum potential VmMin description Minimum value of the membrane potential range 0 0 1 0 name Maximum potential VmMax description Maximum value of the membrane potential range 0 0 1 0 name Midpoint description Midpoint of the sigmoid range 0 0 1 0 name Slope description Slope of the sigmoid range 0 0 1 0 e States name Activity act name Membrane potential vm 15 Appendix Il Synapse types 15 1 Apic
57. f scaling for the color bar right click on the axis to the right size If expand only is ticked the scale will only increase if auto scale is selected you can manually enter the value range You can zoom into the plot by first clicking Figure 30 and selecting the region of interest by moving the cursor while keeping the mouse button pressed To return to full view click into the plot area You can select a region of cells in the Space plot by clicking in the plot area Figure 30 and moving the mouse while holding the left button pressed This region of cells can now be dragged and dropped onto a Time plot Space plot or Data Sampler To change the group associated to the Space plot drop a group onto the plot area or the State Panel Dropping a region of a group has the same effect as dropping the entire group as Space plot always plots the entire group 4 21 4 Time plots A Time plot Figure 31 serves to plot the states of neurons against time To create a new Time plot right click on a group in the diagram editing pane or the browser and select Time plot from the context menu Time plots display the average value of the states of an entire group or region of a group A Time plot can plot several states and states from different groups at once Each state is plotted as a separate trace To add a new group to the Time plot use drag amp drop as described in section 4 27 2 Drag and drop or drag and drop a region from a
58. gr uses the single instance implementation see also Figure 39 For authors of types the drawback is a somewhat more demanding handling of states and update functions To compensate for this great care was taken to provide an easy to use framework for writing types 5 2 1 The StateArray The data structure used to store states of neurons and synapses is the StateArray The structure of a StateArray is depicted in Figure 41 It is used like a two dimensional matrix with the first dimension being the time and the second dimension the index of the individual item neuron or synapse Hence StateArray t n is the value of item n at time t Statearray do 0 Ip StateArray 2 5 to of 2tsf4tspet7 ielej StateArray 0 11 Figure 41 Concept of StateArray Internally StateArrays make use of the valarray class from the standard C library To extract a valarray containing the values for all the items at time t d use std valarray lt double gt va n va StateArrayl d The convention is that StateArray 0 is the current valarray whereas StateArray 2 denotes the valarray that is 2 simulation cycles back in time Valarrays provide a compact syntax for operations on each element in the vector the possibility to apply masks to select specific elements and a couple of other useful features A good reference on the topic is 5 2 2 States in neurons and synapses The subsequently discussed functions are the
59. he path to the external process is shown in the properties dialog of the process 4 15 Creating groups To add a new group to a process firstly activate the process diagram in the diagram editing pane by clicking on the corresponding tab in the tab bar Figure 5 or by double clicking on the process node in the browser Figure 5 Secondly click on the button in the diagram editing pane toolbar Figure 5 The cursor will change to Pr and you can put down the new group by left clicking in the diagram editing pane To abort the action right click in any free space in the diagram editing pane 4 16 Group properties To change the properties of a group either e double click on the group icon in the diagram editing pane e or right click on the group icon or on the group name in the browser and select Properties from the context menu You can change the name of the group as it appears on the diagram or you can add notes by activating the group properties dialog Figure 10 Properties Notes Group Name Grp 1 Color FF ct Meuron current soctneer tnvesnoa3 edit set type Linear threshold Q x Topology M CUITENt type TopologyRect Edit set type Topology Rect T 2 sees Close Figure 10 Group properties dialog Attention After changes in the property dialog you must click on Apply before closing the dialog otherwise the changes will be lost Two additional group properties the neuron type a
60. he red patch CS is detected at some distance when the robot approaches the wall and is followed by a collision with the wall The collision stimulates the proximity sensors thus triggering a US Consequently as in a normal conditioning experiment the CS is correlated with the US predicting it The ISIs of these stimuli are not constant during this conditions due to the variations in the approaching angle to the wall The aim of the experiments with the robot is twofold on the one hand to determine if the circuit can support behavioural associative learning reflected by a reduced number of collisions due to adaptively timed CRs On the other hand to determine if the robot can maintain a memory of the association when re exposed to the same stimuli after the CRs have been extinguished 3 Results 3 1 Results of the Simulated Cerebellar Circuit Acquisition extinction and reacquisition sessions consisting of 100 trials paired CS US CS alone and paired CS US presentations with a fixed ISI of 300 ts 1 ms timestep were performed We have previously shown that the model can acquire the timing of the CR over a range of different ISls 13 As illustrated in Fig 3a the circuit is able to reproduce the same kind of performance usually observed during behavioural experiments CRs are gradually acquired until they stabilize to an asymptotic performance fluctuating around 80 90 of CRs after the third block of paired presentations CRs acq
61. ility Probability 35 OOF Or Or O 36 Probability of output occuring n 37 during a single timestep She Membrane 39 40 Add state variables Al pVvmembrane addStateVariable vm Membrane potential 42 pActivity addOutputState act Activity 43 44 45void 46iqrcommon ClsNeuronIntegrateFire update 47 StateArray amp excitation getExcitatoryInput 48 StateArray amp inhibition getInhibitoryInput 49 StateArray amp vm pVmembrane gt getStateArray 50 StateArray activity pActivity gt getStateArray oul 52 double excGain pExcGain gt getValue 53 double inhGain pInhGain gt getValue 54 double vmPrs pvmPrs gt getValue 55 double probability pProbability gt getValue 56 57 Calculate membrane potential 58 vm 0 vmPrs 59 vm 0 excitation 0 excGain 60 vm 0 inhibition 0 inhGain 61 62 activity iLillProbabilityMask probability s 63 All neurons at threshold or above produce a spike 64 activity Ol vin 0 gt 1 Ol S sO 65 activity 0 i vapo lt 1 0 0 0 66 6 2 Synapses 6 2 1 Header The header file for the synapse shown in Listing 3 is very similar to the one for the neuron The major difference lies in the definition of a state variable that will be used for feedback input 20 Listing 3 Synapse header example l ifndef SYNAPSEAPICALSHUNT HPP 2 define SYNAPSEAP ICALSHUNT HPP 5 A
62. include lt Common Item synapse hpp gt 5 6namespace iqrcommon 7 8 class ClsSynapseApicalShunt public ClsSynapse 9 10 publics 11 ClsSynapseApicalShunt 12 13 void update 14 15 private 16 Hide copy constructor L7 ClsSynapseApicalShunt const ClsSynapseApicalShunt amp 18 19 Feedback input 20 ClsStateVariable pApicalShunt 21 22 fo Pointer tO owtout state 47 23 CIsStateVariable pPostsynapticPotential 24 ee 25 26 27 endif 6 2 2 Source The source code for the synapse is shown in Listing 4 The precompile statement 4 5 at the beginning of the file identifies the synapse type In the constructor 7 we define the output state for the synapse 10 In deviation to neurons a definition of a feedback input 14 using addFeedbacklinput is introduced The remains of the synapse code are essentially the same as for the neuron explained above Listing 4 Synapse code example l include synapseApicalShunt hpp 2 3 5 Interface for dynamic loading is built using a macro AMAKE SYNAPSE DEL TNTERHACHE Iqrcommon TsSynapseApircealShunt 5 ApLcal shunt 6 7iqrcommon ClsSynapseApicalShunt ClsSynapseApicalShunt 8 ClsSynapse 9 10 Add state variables 11 pPostsynapticPotential addOutputState psp Postsynaptic potential 12 13 Add feedback input 14 pApicalShunt addFeedbackInput apicalShunt Apical dendrite shunt 15 16 L
63. input and the threshold Q3 Explain also the role of persistence in the input the membrane potential and the output e Write down the parameters you have used Size cel ee a Econ fC Probebiity Si Ci Membrane persistence o Testo S o Membrane potare OOOO Sempe Oooo o Soman o e Somas e e e Stop the simulation and change the PatternMapped to center Check again the connection plot Q1 Run the simulation watching the plots What was the effect and why Q2 Play with the size of the neuron groups One detail to take into account is whether the different sizes are even or odd Play with this and use the connection plot and change the pattern maps between center and al to see what happens Q3 Try to explain what the difference is Take a look to the manual if you do not know what is going on Write down a brief explanation 10 Tutorial 3 Changing Synapses amp Logging Data 10 1 Aims e Understand and assimilate the basic principles and concepts of iar e Connection type inhibitory e Neuron properties e Arborization e Try out different connection types e Manipulate the states of cell groups at run time and see how the cells are affected e Draw some patterns using the state manipulation panel and play them Save the patterns for future uses e Record simulation data for later analysis using the data sampler 10 2 Building the System e Create a copy of the simulation you used in Tutorial 2 and c
64. is possible to use the membrane persistence to allow faster spiking activity Sigmoid S In this case the transfer function is a sigmoid and there is not possible threshold to set Persistence does matter again 9 3 Building the System e Open the simulation you created in Tutorial 1 Then create a copy to do this is use File Save System As option in the main menu Give it a name like Tutorial 2 Change also the name of the process to something like Tut 2 process e Change the spiking probability of your original RandomSpike cell group slightly e g to 0 1 the exact number does not matter e Create three more neuron groups of 1 single neuron The types for these new groups must be a linear threshold an integrate and fire and a Sigmoid Connection icons Figure 45 Connection Icons e Now create excitatory connections from the RandomSpike cell group to each of your new cell groups using the red connection creation button that appears in Figure 45 The result of the connection scheme is in Figure 46 e Every red line represents a connection Click right button over one of them and choose properties e Change the synapse type it appears at the bottom of the Error Reference source not found for each connection to Uniform fixed weight This means that the weight does not change during the simulation and it will be fixed to certain value if it is set to 1 it means there is no gain or loose of potential during
65. ist should be applied 1 means every pattern 2 means only the first third fifth etc pattern Sending To send the patterns in the list to the group press Send If you have chosen to send the patterns Forever Revoke will stop applying patterns to the group 4 23 Support for work flow of simulation experiments Working with simulations employs a number of generic steps Designing the system running the simulation visualising and analysing the behaviour of the model perturbing the system and tuning of parameters Next to these steps the automation of experiments and the documentation of the model form important parts of the work flow Subsequently we will describe the mechanisms iqr provides to support these tasks Central control of parameters and access from Modules The Harbor When running simulations users frequently adjust the parameter of only a limited number of elements Using the Harbor users can collect system elements such as parameters of neurons and synapses in a central place Figure 35 and change the parameters directly from the Harbor A second function of the Harbor is to expose parameters to an iqr Module All parameters collected in the Harbor can be queried and changed from within a Module Using this method parameter optimization methods can be implemented directly inside iar iqn dhomefubpascate mp igr File Edit few Diagram ate Help MM ee b ur ee fi EW le e400 E neea svsters rew sysem pNe
66. lect the module type from the pull down menu of the Module frame and press Apply Figure 36 If a process has a module associated to it the process icon in the diagram will appear as in Figure 36 Clicking on the Edit button in the Module frame will bring up the module property dialog The parameters for the module vary from type to type and are explained in detail in Appendix Modules Figure 37 Most modules will feed data into and or read from groups Tabs Groups to Module and Module to Groups contain the allocation information between modules and groups The pull down menu next to the name of the reference will list all groups local to the current process In the diagram you can identify a module s group input blue arrow in the top right as shown in Figure 37 or groups with output to a module blue arrow in the bottom right corner Attention For reasons of system consistency all references in the Groups to Module and Module to Groups tab must be specified You can disable the update of a module during the simulation by un checking the Enable Module check box in the process properties dialog of nhapter 3 Tik i ii Sas hal a as Writing User defined Types Ulysses Bernardet 5 Concepts This document gives an overview on how to write your own neurons synapses and modules The first part will explain the basic concepts the second part will provide walk through example implementatio
67. lected cells will be displayed in the Selection window To clear the drawing area and to delete the list press Clear To define a region click on the cell you want to be the first corner in the drawing area move the mouse while keeping the mouse button pressed and release the button when in the opposite corner Once done click on Set to define the region The definition of the region will be shown in To delete a defined region click on Clear 4 19 1 2 PatternMapped In PatternMapped a mapping is used to determine the projection points The procedure is as follows e determine the small group fewer neurons e scale the smaller group to the size of the larger group e determine the coordinates of the cells in the scaled group e apply these coordinates to the larger groups projection point ee SSH SS ES OF Oe sector SeS 4 a ea Figure 22 Which group is mapped onto which depends solely on the size and not on which group is pre or post synaptic We refer to the surface covered by a neuron location in the scaled group when overlaid over the larger group as sector In Figure 22 the concept of the mapping is illustrated As you can see from the depiction projected points need not be at neuron positions Property dialog In the property dialog for PatternMapped you can select e All cells or e a Region of cells from the source and the target population to be used in the mapping The procedure
68. lem How can we set a threshold and let pass only the things that are below it A solution is to invert the original image inverting an image results in the negative image as the negatives produced by old fashioned cameras where the darker spots are now the clearer In our case we obtain a group whose output is the negative of the original group s output higher values are now encoded with lower values and vice versa The file Negative igr shows how this can be simply done using three groups Before proceeding we have to consider that IQR has certain limitations inspired by biological neural networks in IQR standard neurons only encode positive values Therefore in iqr the inversion of a certain positive value x can t be coded as x because a neuron s activity can t be negative Then a first constrain for our operation is that the result of the inversion has to be positive A second desirable feature is to preserve the range of the output values An example if the pixel values of the original image range from 0 to 1 then we want the pixel values of the negative image to range from 0 to 1 as well The simpler inversion operation that respect both constrains is 1 x Formula Negative 1 Original 13 2 Implementation Processes again we will use just one process camera with its properties set as in Tutorial 1 Groups we need 3 groups Dummy Original Negative and Bias We send the output of the came
69. ment the ones we investigated so far Different forms of plasticity have been discovered in almost all the cerebellar synapses 26 and they could be the loci of storage of long term memories as well In a different modelling study Kenyon 27 for example proposed that memory transfer could take place in the cerebellar cortex due to bi directional plasticity at the synapses between parallel fibres and the inhibitory interneurons but this direct dependence has not been confirmed In relation to other models of the cerebellum the present approach lays between purely functional models 28 and more detailed bottom up simulations 7 It embeds assumptions already implemented in those models like the dual plasticity mechanism but with the aim of keeping the model minimal in order to allow simulations with real world devices In future work we aim to investigate other alternative hypothesis of memory transfer to be tested with robots in more realistic tasks Acknowledgments This work has been supported by the Renachip FP7 ICT 2007 8 3 FE BIO ICT Convergence and eSMC FP7 ICT 270212 projects References 1 Napier R Macrae M Kehoe E Rapid reaquisition in conditioning of the rabbits nictitating membrane response Journal of Experimental Psychology 18 2 182 192 1992 2 Mackintosh N The psychology of animal learning Academic Press London 1974 3 Gormezano l Schneiderman N Deaux E Fuentes l Ni
70. more counterbalanced by the LTD Some residual CRs are still expressed for longer ISIs and are more resistive to extinction i e trial 38 and 52 This is explained by the fact that for longer ISIs a pause can still be expressed by PuSo allowing the A N to rebound Finally a re acquisition experiment was performed to investigate whether a new paired CS US training session leads to a faster expression of the first CR As illustrated by the learning curve in Fig 4c a CR is already expressed during the first block of CS US presentations while the performance of the system stabilizes during the second block of trials As observed in the simulation results the faster expression of the first CR is due to the residual plasticity in the mf A N synapse that allows for a faster repolarization of the AIN membrane potential Consequently a more robust rebound can be observed whenever PuSo releases AIN from inhibition When compared to the acquisition session the performance also appears more stable both in terms of USs avoided and latencies to the CR Fig 4f Since during the acquisition session the mf AIN synapse is still undergoing potentiation and a robust rebound can not be elicited until late during the training weaker CRs are elicited that can not fully inhibit the IO More LTD events are then induced at the pf Pu synapse and a shortening of the latency is observed 4 Discussion Here we investigated the question whether the A N provides the subs
71. n e Parameters name Clip potential Clip description Limits the membrane potential to values between VmMax and VmMin Parameters maximum potential VmMax minimum potential VmMin options true false name Excitatory gain ExcGain description Gain of excitatory inputs The inputs are summed before being multiplied by this gain range 0 0 10 0 name Inhibitory gain InhGain description Gain of inhibitory inputs The inputs are summed before being multiplied by this gain range 0 0 10 0 name Membrane persistence VmPrs description Proportion of the membrane potential remaining after one time step if no input arrives range 0 0 1 0 name Minimum potential VmMin description Minimum value of the membrane potential range 0 0 1 0 name Maximum potential VmMax description Maximum value of the membrane potential range 0 0 1 0 name Probability Prob description Probability of output occurring during a single time step range 0 0 1 0 name Threshold potential ThSet description Membrane potential threshold for output of a spike range 0 0 1 0 name Spike amplitude SpikeAmpl description Amplitude of output spikes range 1 0 1 0 name Membrane potential reset VmReset description Membrane potential reduction after a spike range 0 0 1 0 e States name Activity direction output 14 4 Sigmoid The iqr sigmoid cell type is based on the perception cell model oft
72. n responsible for the activation of the CR pathway downstream of the deep nucleus and for the inhibition of the JO A variant of a generic Cerebellar Memory Consolidation and Savings 325 integrate and fire neuron was used to model rebound excitation of the AIN The membrane potential of neuron at time t 1 V t 1 is given by V t 1 8 V t A V t oe A e Vi t 1 H R 1 i i l 7 di where 6 is the rebound threshold and wu is the rebound potential The potential of AIN is kept below 6 by tonic input from PuSo When disinhibited AIN membrane potential can repolarize and if the rebound threshold is met the membrane potential is set to a fixed rebound potential A N is then active until it is above its spiking threshold Simulation of Plasticity Two sets of connections undergo bi directional plasticity 1 pf Pu synapse defined as the connection strength between PuSy and PuSo 2 mfHAIN synapse defined as the connection between Po and AIN can both undergo long term potentiation LTP and long term depression LTD In order to learn the pf Pu synaptic efficacy has to be altered during the conditioning process LTD can occur only in the presence of an active CS driven stimulus trace Apusy gt 0 coincident with cf activation 22 At each time step the induction of LTD and LTP depends on ew t if Apysy gt O amp Avg gt O LTD wWigl E 1 w t n wie Wiz t if Apusy gt O LIP
73. n also notice it if you open a connection plot You better avoid to open a connection plot when it contains so many synapses It could hang your IQR for more than a minute 13 3 Links to other domains Image processing You can find this function implemented in Photoshop It is simply called invert A difference is that photoshop does this in a color image whereas we only did it for a grayscale image However we could attain the same result working with three channels RGB and inverting each of them separately and then we sent them to an output module Biology systems controlled by dis inhibition Biological neurons work as thresholded elements They fire when the internal electrical charge exceeds a tipping point Real neurons can not change the sign of their output this is Known as the Dale s principle they can be excitatory or inhibitory but they can not have both effects Nonetheless some brain areas perform an inversion computation The way it is achieved is as we did it in this exercise a persistently active neuron s is controlled by an inhibitory neuron s These circuits are said to work by dis inhibition An example is found in the cerebellum The Cerebellar Cortex controls the Cerebellar Nuclei by dis inhibition when the cerebellar cortex stops the cerebellar nuclei starts and vice versa 13 4 Exercises e Set the missing parameters in the file negative iqr in order to have the simulation running Report
74. n of the motor size 3 16 3 Serial VISCA Pan Tilt Operating system Linux This module can control VISCA based pan tilt cameras such as the Sony EVID100 A Pan tilt device is a two axis steerable mounting for a camera Pan is the rotation in the horizontal tilt the rotation in the vertical plane Figure 59 a e Parameters name Camera ID description ID of the camera can be set on the camera itself default 7 name Pan Tilt Device description Path to Serial Port default dev ttyS0O name Pan Tilt Mode description Pant Tilt Mode range options relative or absolute name Nr Steps Pan description Number of steps for pan in relative mode range 1 1000 name Nr Steps Tilt description Number of steps for tilt in relative mode range 1 1000 e States name Pan Tilt Input direction Group Module description In relative Pan Tilt size 2 2 tilt a Panning and Tilting 6 Pan tilt motion Figure 59 Pan Tilt Mode In Figure 59 b the association between the cells in the Pan Tilt Input group and the motion of the pan tilt device is shown In absolute mode the activity of the cells in the Pan Tilt Input group defines the absolute position of the device Pan Acta Acti Tilt Actu Act 2 If the mode is set to relative the pan and the tilt angles are increased based one the cell s activity in the Pan Tilt Input group Apan Act ACti1 1 PanFactor Asr Acta 2 Acto
75. nd fast creation of complex systems and on the other hand the constraints that guide the construction of biologically realistic models The second point is especially important in education where the introduction into a biological language is highly desirable Low level simulators are not in the same domain as iqr as in their heuristic approach detailedness is favored over size of the model and speed of the simulation 2 1 Models in igr system 1 Process gt 1 iey gt 21 e Neuron gt 1 e Module 0 1 hangin gt 0 e Synapse gt 1 a b Figure 3 The structure of models in igr Models in igr are organized within different levels Figure 3 the top level is the system which contains an arbitrary number of processes and connections Processes in turn consist of an arbitrary number of groups On the level of processes the model can be broken down into logical units This is also the level where interfaces to external devices are specified A group is defined as a specific aggregation of neurons of identical type specific in terms of the topology of the neurons in the group being a property of the group Connections are used to feed information from group to group A connection is defined as an aggregation of synapses of identical type plus the definition of the arrangement of the synapses framework group connection L F group Figure 4 The group and connection framework
76. nd the group topology will subsequently be explained in more detail 4 16 1 Group neuron type By default a newly created group has no neuron type associated to it To select the neuron type for the group take the following steps e select the neuron type from the pull down list Figure 100 e press the Apply button Figure 10 e Now change the parameters for the neuron type by clicking on Edit Figure 10 An extended explanation of the meaning of these parameters for each type of neuron is given in 14 Appendix I Neuron types 4 16 2 Group topology The term topology as used in this manual refers to the packing of the cells within the group The basic concept behind it refers to cells in a group being arranged in a regular lattice To define the group s topology proceed as follows e select the topology type from the pull down list e press Apply e press Edit Topology types If we refer to a TopologyRect every field in the lattice is occupied by one neuron TopologyRect is therefore defined by the Width and the Height parameters In case of JopologySparse the user can define which fields in the lattice are occupied by neurons In Figure 11 the dialog to define the TopologySparse a and the graphical representation of the defined topology b are shown To enter a new location for a neuron e select add row and e enter the x and y coordinates in the appropriate field of the table To delete neurons e select the row
77. nection in the diagram editing pane and select Connection plot from the context menu The State Panel for Connection plots is slightly different than for other plots You can select to display the static properties of a connection i e the Distance or Delay or the internal states by selecting Synapse and one of the states in the list To visualize the synapse value for the corresponding neuron click on the cell in the source or the target group Attention Connection plots do not support drag amp drop Fo Gonnection Plot for New Group eNew Group 2 a New Group 3 New Group 2 Distance 3 Delay C5 Synapse States Y axnlin C psp Figure 32 igr Connection plot 4 21 6 Data Sampler The Data Sampler Figure 33 is used to save internal states of the model To open the Data Sampler use the menu Data Data Sampler A newly created Data Sampler is not bound to a group To add groups or regions from groups use drag amp drop as described earlier Data Sampler exci n inhIn Acquisition modIn Continuous act oss E Owm E gt Append Ci Sequence Misz auto start stop oo live data Figure 33 Data Sampler Like for the Time plots the State Panel for the Data Sampler indicates which region of the group is selected Moreover you will find an average check box which allows you to save the average of the entire group or region of cells instead of the individu
78. ning is conserved as demonstrated by the faster reacquisition of the CR when the CS is again paired with the US 1 A large number of lesion inactivation and electro physiological studies generally agree that classical conditioning of motor responses is strictly dependent on the cerebellum see 4 5 for a comprehensive review However the relative contributions of the cerebellar cortex and the deep nucleus in the acquisition and retention of the CR still remain quite elusive A change in synaptic efficacy seems to be involved in both sites and to be responsible in controlling different aspects of the CR One hypothesis 6 7 is that two mechanisms of plasticity at cortical and nuclear level jointly act to regulate different sub circuits fast and slow involved in the acquisition and retention of the CR In a sort of cascade process a faster sub circuit including the Purkinje cells the deep nucleus and the inferior olive 1 is responsible for learning a properly timed CR as indicated by the fact that lesions of the cerebellar cortex produce responses which are disrupted in time 8 9 10 and 2 it signals to the second circuit that the two stimuli are related A slower sub circuit involving the Pontine nuclei the deep nucleus and the Purkinje cells driven by the faster one regulates then the expression of the CR and stores a long term memory of the association In the context of the Distributed Adaptive
79. ns and the appendix lists the definition of the relevant member variables and functions for the different types iqr does not make a distinction between types that are defined by the user and those that come with the installation both are implemented in the same way The base classes for all three types neurons synapses and modules are derived from Cisitem Figure 38 Iiqrcommon Clshlodule ez iqrcommon ClsThreadModule Iqrcommon Clsheuron Iqrcammon Cle synapse Figure 38 Class diagram for types Iqrcommon Clsitem fee The classes derived from Clsltem are in turn the parent classes for the specific types a specific neuron type will be derived from ClsNeuron a specific synapse type from ClsSynapse In case of modules a distinction is made between threaded and non threaded modules Modules that are not threaded are derived from ClsModule threaded ones from ClsThreadModule The inheritance schema defines the framework in which parameters are defined e data is represented and accessed input output and feedback is added All types are defined in the namespace iqrcommon 5 1 Object model To write user defined types it is vital to understand the object model iqr uses Figure 39 shows a simplified version of the class diagram for an iqr system The lines connecting the objects represent the relation between the objects The arrow heads and tails have a specific meaning 2
80. nts a unique documentation of iqr a simulator for large scale neural systems that provides a mean to design neuronal models graphically and to visualize and analyze data on line We are grateful to the laboratory of Synthetic Perceptive Emotive and Cognitive Systems SPECS which was involved in the development of this multi level neuronal simulation environment We wish to express our appreciation to all the students that successfully used iqr in their research projects and helped to improve it further Finally we would like to thank Anna Mura and Sytse Wierenga for designing the book cover Image adapted from the human connectome data simulated using iqr in the context of the project Collective Experience of Empathic Data Systems CEEDS FP7 ICT 2009 9 ay m Chapter 1 E P Ta Introduction to iar Ulysses Bernardet Paul Verschure 1 Epistemological background The brain is an extraordinarily complex machine The workings of this machine can be described at a multitude of levels of abstractions and in various description languages Figure 1 These description levels range from the study of the genome and the use of genes in genomics the investigation of proteins in proteomics the detailed models of neuronal sub structures like membranes and synapses in compartmental models the networks of simple point neurons the assignment of function to brain areas using e g brain imaging techniques th
81. odel comiats dh Figure 2 In the Convergent Validation method modelling is regarded as an iterative process At the initial stage of model building only a subset of all possible constraints can be taken into account The convergent validation itself consists of an iterating between validating the model against a set of constraints and adapting the model to be compliant with these constraints Examples of construction constraints are the operation that neurons can perform and the known neuronal architecture Validation constraints include the behaviour of the biological system e g the behaviour of a rat in a water maze When evaluating the system it is important to define an a priori target criteria that the actual behaviour of the system can be compared to The validation comes at different levels of rigour In the weak form the validation consists of assessing whether the system is at all able to perform a given task in the real world The stronger form of validation consists of comparing the mechanisms underlying the behaviour of the synthetic system i e the manner in which a task is solved with a biological target system Biology provides validation criteria of the weak type in the form of ethological data and in the stronger form as neurobiology boundary conditions 1 2 1 Real world biorobotics The usage of robots in cognitive sciences has been heralded at the start of the 20 century by Hull and Tolman Hull undertook the c
82. ogy 3 1935 1950 2007 i ppr T i E AT T z 7 5 ee i T az e I n a Spe a gm E ee aaa s a References Pr ae re i io i G en Baernstein H D amp Hull C L 1931 A mechanical model of the conditioned reflex Journal of General Psychology 5 99 106 Hull C L 1952 A Behavior System An introduction to behavior theory concerning the individual organism New Haven Yale University Press Hull C L amp Krueger R G 1931 An electro chemical parallel to the conditioned reflex Journal of General Psychology 5 262 2609 Hull C L amp Baernstein H D 1929 A mechanical parallel to the conditioned reflex Science 0 1801 14 15 Josuttis N M 1999 The C Standard Library A Tutorial and Reference p 832 Addison Wesley Professional Retrieved from http www amazon com The Standard Library Tutorial Reference dp 0201379260 Shepherd G M 2003 The Synaptic Organization of the Brain Oxford University Press Tolman E C 1939 Prediction of vicarious trial and error by maens of the schematic sowbug Psychological Review 46 318 336 Verschure P F M J Voegtlin T amp Douglas R J 2003 Environmentally mediated synergy between perception and behaviour in mobile robots Nature 425 620 624 Verschure Paul F M J 1997 Connectionist Explanation Taking positions in the Mind Brain dilemma In G Dorffner Ed Neural Networks and a New
83. onIntegrateFire public ClsNeuron 9 10 publics ial ClsNeuronIntegrateFire 12 13 void update 14 ES private 16 mide eop Constructor 7 L7 ClsNeuronIntegrateFire const ClsNeuronIntegrateFire amp 18 19 Pointers tO parameter objecte 20 ClsDoubleParameter pVmPrs aM ClsDoubleParameter pProbability pThreshold 22 Zs gt Pointers to stake variables 24 ClsStateVariable pVmembrane pActivity AS be 26 2T 28 endif 6 1 2 Source Next we ll walk through the implementation of the neuron which is shown in Listing 2 Line 4 5 defines the precompile statement that iqr uses to identify the type of neuron see section 3 2 3 In the constructor we reset the two StateVariables 9 10 On line 12 38 we instantiate the parameter objects see section 3 1 and at the end of the constructor we instantiate one internal state 41 with addStateVariable and the output state 42 with addOutputState As for all types the constructor is only called once when iqr starts or the type is changed The constructor is not called before each start of the simulation The other function being implemented is update 46 Firstly we get a reference to the StateArray for the excitatory and inhibitory inputs 47 48 See section 1 2 2 For clarity we create a local reference to the state arrays 49 50 Thus the state array pointers need only be dereferenced once which enhances performance For e
84. onfigurations You can save the arrangement and the properties of the current set of plots and Data Sampler To do so e go to Data Save Configuration e select a location and file name To read an existing configuration file select Data Open Configuration Attention If groups or connections were deleted or sizes of groups changed between saving and loading the configuration the effect of loading a configuration is undefined 4 22 Manipulating states The State Manipulation tool as shown in Figure 34 serves to change the activity Act of the neurons in a group You can draw patterns add them to a list play them back with different parameters So a ea gt y Ta F p n A lus Eg ime Nev Group L tt Value 1 000 e a a m Mode e Clamp amp ma 5 gt Multiply Pay Bak gt For ever gt Times 1 IF Interval i B 1 i Step Size IL F m 5 T cise mle el Figure 34 State Manipulation tool Primer As the State Manipulation is a fairly complex tool we will start with a step by step primer The details are specified further below e create a pattern by selecting from to draw in the drawing area e add the pattern to the list press Send Attention Only patterns in the pattern list are sent to the group Drawing a pattern To create a new pattern e set the value using the Value spin box in the drawing toolbar e select the
85. onstruction of the psychic machine by applying a framework of physics to psychology Tolman exploring an alternative conceptual route and striving at uniting the methods of behaviourism with the concepts of Gestalt psychology in 1939 proposed the robot Sowbug The shortcoming of many of the above mentioned approaches is that they are lacking a clear conceptualisation of the employment of robots in biological sciences We see the main gain of using robots in the fact that the real world provides clear constraints at both the construction and validation level The properties of the elements of the model have to obey the laws of physics in their construction as well as in the interaction with the real world If the agent is to operate in the real world the mechanical properties have to take into account inertia friction gravity energy consumption etc Moreover acquiring information from the environment will have a limited bandwidth and the data will most likely be noisy One of the key properties of biorobotics approach is that it circumvents the problem of the low degree of generalizability of models developed in simulation only This problem stems from the limited input space to the model and the hidden a priori assumptions about the environment in which the system is to behave Support for real world systems and the convergent validation method is the first cornerstone design philosophy behind tar 1 2 2 Large scale models lf
86. or SVG image To do so use the menu Diagram Save The graphics format will be determined by the extension of the filename Hint If you choose SVG as the export format the diagram will be split into several files due to the SVG specification It is therefore advisable to save the diagram to a separate folder To print the diagram use the menu Diagram Print 4 7 igr Settings Via the menu Edit Settings you can change the settings for iar General e Auto Save Change the interval at which iqr writes backup files The original files are not overwritten but auto save will create a new backup file the name of which is based on the name of the currently open system file with the extension autosave appended e Font Name Set the font name for the diagrams The changes will only be effective at the next start of iqr e Font Size Set the font size for the diagrams The changes will only be effective at the next start of iqr NeuronPath SynapsePath ModulePath The options for NeuronPath SynapsePath ModulePath refer to the location where the specific files can be found As they follow the same logic the description below applies to all three e Use local Specifies whether iqr loads types from the folder specified by Path to local below e Use user defined Specifies whether iqr loads types from the folder specified by Path to user defined below e Path to standard The folder into which the types were store
87. otor map For each cell the corresponding direction and the speed of the left red and right blue motor is shown a Tabular Motor map b Vector Motor map Figure 56 Moto map types used for the Khepera and e puck robots In Vector motor map Figure 56 b vector for the direction and speed of motion is computed from the activity of all cells in the Motor Input group Motoren Act 1 1 Act 7 2 MaxSpeed2 Motofjignt Act 21 ACt 22 MaxSpeed2 Infra red sensors KRobots are equipped with infra red sensors These sensors are used in two ways in passive mode they function as light sensors in active mode they emit infra red light and measure the amount of reflected light which allows the robot to detect nearby objects In Figure 57 the characteristics of the passive and active mode of the sensors is shown Output of the passive sensing is fed into the Ambient Sensors cell group active mode sensor readings into the Proximal Sensors cell group E black plastic grey plastic COP PEt wood 1 e wd 2 while plastic pink sponge green styropor Measured value Measured reflexion value i 10 20 30 40 Bi al Light intensity Distance to the wall mm a Ambient light b Object proximity Figure 57 Khepera Infra red sensors characteristics 16 2 2 Video Operating system Linux Windows
88. ows the source origin of the arrow and the target arrow end of the connection This means that one spike in a neuron or group of neurons of the source is propagated through the connection to another neuron or group of neurons of the target making them to spike according to the neuron transfer function threshold etc Best thing is to create bigger groups of neurons and define different patterns to check this tool 9 4 Exercise e Bring up space and time plots for each cell group and start the simulation Every one has the name of the group so check if you can identify each one What do you see A Connection between Random Spike RS and Linear Threshold LT groups Q1 Space plot is the cell spiking continuously Q2 Time plot is it following the source Why Note think of the pattern map and also in the definition of LT neuron B Connection between RS and Integrate and Fire IF groups Q3 Space plot why is all the time spiking Q4 Time plot is it following the source Why C Connection between RS and Sigmoid Q5 Space plot why is all the time spiking Q6 Time plot is it following the source Why e Inside the properties of the neuron groups play with the excitatory gain threshold and membrane persistence check the time and space plots the option vm that is the membrane potential Q1 Explain what these parameters do Q2 Explain what is the membrane potential and its relation with the
89. pse 2 3 Simulated Conditioning Experiments Our simulations implement an approximation of Pavlovian conditioning protocols 3 and their impact on the cerebellar circuit CS events triggered in the direct pathway Po mf collaterals A N are represented by a continuous activation of the mossy fibres lasting for the duration of the CS In the indirect pathway Po Gr pf Purkinje cell CS events are represented by a short activation of pf corresponding to 1 ts at the onset of the CS US events are represented by a short activation 1 ts of the cf co terminating with the CS The behavioural CR is simulated by a group of neurons receiving the output of the A N group The performance of the circuit is reported in terms of frequency of effective CRs A response is considered an effective CR if after the presentation of the CS the CR pathway is active before the occurrence of the US and the A N is able to block the US related response in the JO To avoid the occurrence of un natural late CRs as observed in the previous study due to the long persistence of the eligibility trace a reset mechanism is introduced to inhibit the CS trace whenever the US reaches the cerebellar cortex 2 4 Robot Associative Learning Experiments In order to assess the ability of our model to generalize to real world conditions and a variable set of inter stimulus intervals ISIs the performance of the model has been evaluated interfacing the cir
90. put arrives range 0 0 1 0 name Clip potential Clip description Limits the membrane potential to values between VmMax and VmMin Parameters maximum potential VmMax minimum potential VmMin options true false name Minimum potential VmMin description Minimum value of the membrane potential range 0 0 1 0 name Maximum potential VmMax description Maximum value of the membrane potential range 0 0 1 0 name Probability Prob description Probability of output occurring during a single time step range 0 0 1 0 name Threshold potential ThSet description Membrane potential threshold for output activity range 0 0 1 0 e States name Membrane potential vm name Activity act 14 3 Integrate amp fire Spiking cells are modeled with an integrate and fire cell model The membrane potential is calculated using the following equation The output activity of an integrate and fire cell at time f 1 a t 1 is given by Sag ay SpikeAmpl with probability Prob for v f 1 gt ThSet LF G otherwise where SpikeAmpl is the height of the output spikes ThSet is the membrane potential threshold Rand 0 1 is a random number and Prob is the probability of activity After cell produces a spike the membrane potential is hyperpolarized such that n t 1 u t 1 VmReset where v t 1 is the membrane potential after hyperpolarization and VmReset is the amplitude of the hyperpolarizatio
91. r Second CPS to 25 Press tab key and then OK CHECK open again the same menu to make sure CPS is set as you wanted e Check also Sync Plots to make sure every event is represented in real time in the space and time plots Step 2 Press the Play button to start the simulation e Press right button over the neuron group and bring up the space plot to watch the cells spiking don t forget to select the live data check box and choose the cell state variable that you want to watch activity means only when the cell spikes In brief the space plot shows the state of each cell in a group in the plot area Q1 What do you see Which is state is being represented Is it similar to the bottom diagram in Figure 44 Q2 What does each small square represent Q3 Can you see activity of two different times in space plot or it is just instantaneous information e Press right button again over the neuron group without quitting the space plot and bring up a time Plot as well Check again live data and the same cell state variable as the space Plot In brief time plot shows the states of neurons against time Q1 Explain what you see Q2 Are you watching in the time plot the activity of one neuron the average of the whole group of the total activity of the whole group e Try to play with the properties of the neuron group such as Probability Spike Amplitude and Size of the Group STOP the simulation before changing tho
92. ra module to the Original group as we did in the previous tutorial This group is connected to the group Negative where we will obtain the negative image For the inversion operation we require the bias group as well that will play the role of the 1 in the formula For technical reasons we still need the Dummy group Space Plot for Negative lt 2 gt j E Negative states excin H inhin modin O wm act W live data Original states excin inhin gt modin _ vm act iw live data Figure 53 igr circuit original mug bottom and negative mug top We connect Original and Negative with an inhibitory connection As both groups have the same size by adding a connection we will obtain 1 to 1 links between the neurons in both groups this means that a neuron in a certain position within the source group will send its output to a neuron in the same position in the target group This is the default setup it can be modified using the connection properties as we will see later However the default is setup is the one we want for this connection The result we obtain after the linking these two groups is that the higher the value in Original the lower the value in Negative and vice versa At this point we are still not done because the values in Negative are negative we are only obtaining x and not 1 x For the Negative group we still need a default excit
93. rization is actually created E g a probability of 5 means that the chance a synapses Is created is 50 4 19 3 DelayFunction The delay function is used to compute the delay for each synapse belonging to an arborization In most delay functions the calculation is depending on the size of the arborization i e height width or outer height outer width respectively The possible delay functions are e FunLinear e FunGaussian e FunBlock e FunRandom e FunUniform Figure 25 shows the meaning of the parameters for the three least trivial functions Delay function Linear min 0 max 5 Delay function Gaussian min 0 max 5 distmay 1 sigma 0 5 Delay signa 0 9 m sigma 1 6 sigma 2 8 Distance b Gaussian Function Delay distmay 2 5 dist 4 diStmar 5 5 Distance a Linear Function Delay function Block min 0 max 5 width 4 Delay Distance c Block Function Figure 25 Delay functions In FunRandom the delays are randomly distributed between O and max Distance is not taken into account The same holds true for FunUniform where all synapses have the same delay of value 4 19 4 AttenuationFunction The attenuation function is used to compute the attenuation of the signal strength for each synapse belonging to an arborization The larger the attenuation the weaker the signal will be that arrives at the soma In most attenuation functions the calcul
94. rray amp excitation getExcitatoryInput StateArray amp inhibition getInhibitoryInput StateArray amp modulation getModulatorylInput The user is free as to which of these functions to use 5 2 2 2 State related functions in synapses Synapses also must have access to the input state which is actually the output state of the pre synaptic neuron The implementation of the pre synaptic neuron type thus defines the input state of the synapse To access the input state the function getInputState is employed which returns a pointer to a ClsStateVariable StateArray amp synin getInputState gt getStateArray To use feedback from the post synaptic neuron use the addFeedbacklnput function CIsStateVariable pApicalShunt pApicalShunt addFeedbackInput apicalShunt identifier apical dendreice shunt descorciprcion s StateArray amp shunt pApicalShunt gt getStateArray 5 2 3 Using history To be able to make use of previous states i e to use the history of a state you explicitly have to declare a history length when you create the StateArray using the addStateVariable addOutputState or addFeedbacklinput functions The reference for the syntax is given in the Appendix l Neurons types and Appendix_Il _ Synapses 5 2 4 Modules and access to states Unlike neurons and synapses modules do not need to use internal states to represent multiple elements Modules require
95. rs when the process is created If you click on it it shows the contents of that process i e groups and connections as shown in Figure 43 e Click the Tut 1 Process tab Start adding groups that will performance inside the process by clicking on the group icon e Now edit the properties of the new group and set e Set Group Name as RandomSpikeGroup e Set the type of the Topology as TopologyRect and click there edit Make it 10 cells wide and 10 cells high it will be used again in later exercises Note Topology refers to the packing of the cells within the group In this case TopologyRect means that every field in the lattice is occupied by one neuron File Edit Connections D go New Process 2 Provess Sane Mew Process 7 Enable Module External Path Figure 42 Process creation and process properties P Tut 1 Process Group Name New Group 2 J Color Neuron current type Linear threshold set type Linear threshold Topology current type TopologyRect edit set type TopologyRect Apply Close Figure 43 Group creation inside a process and group properties Set the Neuron type the type of the neuron to RandomSpike Then press edit and give ita spiking Probability of 0 42 and set the Spike Amplitude to 1 0 8 4 Exercise Step 1 Set cycles per second CPS to 25 e Go to File System Properties Set Cycles Pe
96. ry sizes of state arrays or to throw a ModuleError in the init function if the size criteria is not met 5 2 4 1 Access protection If a module is threaded i e the access to the input and output states is not synchronized with the rest of the simulation the read and write operations need to be protected by a mutex The ClsThreadedModule provides the qmutexThread member class for this purpose The procedure is to lock the mutex to perform any read and write operations and then to unlock the mutex qmutexThread gt lock Te any operations that accesses the input or the output state i qmutexThread gt unlock As the locked mutex is impairing the main simulation loop as few as possible operations should be performed between locking and unlocking Failure to properly implement the locking access and unlocking schema will eventually lead to a crash of iar 5 3 Defining parameters The functions inherited from Clsltem define the framework for adding parameters to the type Parameters defined within this framework are accessible through the GUI and saved in the system file To this end iqr defines wrapper classes for parameters of type double int bool string and options list of options 5 3 1 Usage The best way to use these parameters is to define a pointer to the desired type in the header and to instantiate the parameter class in the constructor using the add Double Int Bool String Options Parameter fun
97. se parameters Q1 What does that Probability means Q2 What happens if you set probability to 1 Q3 What happens in the space plot if you change spike amplitude And in the time plot Q4 What about the size of the group Describe the effects in both type of diagrams Q5 Can you perceive any substantial difference in the space plot e Now drag only 1 cell from the space plot in the time plot Q1 What do you see Q2 What about dragging a group of 4 cells for example Q3 Does it give you the sum of the individual activities of the average of the selected group Play with different combination of cells to discover it e What does the CPS in the bottom left corner of the window mean Change the values in File System Properties and check how the simulation speed changes e g CPS 1 CPS 2 CPS 10 Take care because CPS 0 gives you the maximum speed the machine can give and it might freeze the system Q1 What is a CYCLE in IQR Q2 Can you see different cycles at the same time in a space Plot Q3 Can you see different cycles at the same time in a time Plot ae Time Plot Amplitude Time scale cycles Group size pm Scale Figure 44 Time and space plots 9 Tutorial 2 Cell Types Synapses amp Run time State Manipulation 9 1 Aims e Understand and assimilate the basic principles and concepts of iqr e Neuron groups Random spike Linear threshold Sigmoid Integrate and Fire e
98. se the plasticity so that reacquisition will require the same amount of conditioning trials as in the na ive circuit to express the first CR This is in agreement with the findings of a graded reduction in the rate of reacquisition as a function of the number of extinction trials in conditioned rabbits 24 25 3 2 Robotic Experiments Results Cerebellar mediated learning induced significant changes in the robot behaviour during the collision avoidance task Learning performance during acquisition training directly follows the results observed in the previous study 13 Initially the behaviour is solely determined by the URs the robot moves forward until it collides with the wall and a turn is made While training progresses the robot gradually develops an association between the red patch CS and the collision US that finally is reflected in an anticipatory turn CR The overall performance of the system during acquisition is illustrated by the learning curve in Fig 4a The CRs frequency gradually increases until it stabilises to a level of 70 CRs after the fourth block of trials The changes in ISI and CR latencies over the course of a complete session help explaining the learning performance of the model Fig 4b During the first part of the experiment the CS is always followed by a collision US with latencies between 70 90 ts The first CR occurs with a longer latency that can not avoid the collision with the wall and consequently
99. shop If you ever worked with an image processing software like Adobe Photoshop you can find this manipulation in the program Indeed in Photoshop its name threshold and it does exactly the same as our exercise The difference is that what you do in Photoshop for one image in IQR you do it to a stream of images in real time 12 5 Exercises e Complete the IQR module with the missing parameters Report them e Change the Integrate and Fire neuron to a Linear threshold neuron persistence and probability set to O and 1 respectively Set the threshold to the same value you used for the Integrate and Fire neuron Describe the result 13 Tutorial 5 Negative Image In this tutorial we will learn how to invert the activity of a group We will see that if we apply this operation to the camera output we can obtain a negative image The result of this tutorial is implemented in the file negative iqr However the file is not complete As an exercise you have to set the missing parameters while reading the tutorial 13 1 Introduction In the previous tutorial we searched for the brightest spot in an image This task was easy because the video module represents brighter spots with higher values Therefore we could just use an Integrate and Fire laF neuron and set a threshold so that only neurons with higher inputs were active But had we searched for the darker spots instead of the brighter spots then we would had faced a prob
100. sion delay is dependent on the length of the dendrite e any back propagating signals are limited to the dendrite In principle the complete definition of a connection is twofold in that it comprises of the definition of the update function of the synapse and the definition of the connectivity In this context the term connectivity refers to the spacial layout of the axons synapses and dendrites But update function and connectivity are not as easily separable as the delays in the dendrites are derived from the connectivity Multiplicity As mentioned above a single connection is an assembly of axon synapse dendrite nexuses A single nexus in turn can connect several pre synaptic neurons to one post synaptic cell or feed information from one pre synaptic into multiple post synaptic neurons A nexus can hence comprise of several axons synapses and dendrites In Figure 13 two possible cases of many to one and one to many are diagrammed The first case a can be referred to as receptive field RF the second one as projective field PF distance __ _j qistance a Receptive field RF b Projective field PF Figure 13 The axon synapse dendrite nexus and distance For reasons of ease of understanding the connectivity depicted in Figure 13 is only one dimensional i e the pre synaptic neurons are arranged in one row This is not the standard case most of the times a nex
101. stands for a relation where A contains B 0 5 1 3 Eory Figure 39 Simplified class diagram of an iqr system The multiplicity relation between the objects is denoted by the numbers at the start and the end of the line E g a system can have O or more processes 0 near the arrow head A process in turn can only exist in one system 1 near the On the phenomenological level to a user a group consists of a n 2 1 neuron s and a connection of n 2 1 synapse s In terms of the implementation though as can be seen in Figure 2 a group contains only one instance of a neuron object and a connection only one instance of a synapse object This is independent of the size of the group or the number of synapses in a connection 5 2 Data representation In the concept of iqr neurons and synapses do have individual values for parameters like the membrane potential or weight associated to them In this document type associated values that change during the simulation are referred to as states There are essentially two ways in which individual value association can be implemented e multiple instantiations of objects with local data storage Figure 40a or single instance with states for individual objects in vector like structure Figure 40b state 0 a Multiple instantiations local storage b Single instance storage Figure 40 Representation of data in igr types For reasons of efficiency i
102. t if persistence is set to 0 this parameter has no effect but again set this value to 0 unless you have good reasons to do otherwise threshold this is the only parameter that matters for this exercise By setting its value we decide the extent of the active are that we will see in the image Space Plot for CameraQutput i fen CameraQutput states _ 0 75 APE inhin O modin O vm 0 5 i J act 0 50 100 150 iv live data Space Plot for CameraOutput lt 2 gt SSS i Tea CameraQutput states excin inhtin modin ni vm 0 5 act I W live data 0 293 I L30 Figure 52 Face of the bearded author of this exercise with a messy office landscape as background The output of the camera appears in green in the top right The yellow space plot shows the result of the thresholding process As you will see IQR does a good job in processing the data at real time Keep in mind that you CAN modify parameter values while an IQR model is running you can not modify the neuron types connections etc Changes to parameters are effective once you press the apply button In this exercise you can modify the threshold value up and down until you get your favorite output Test the O and 1 cases 12 4 Links to other domains Maths In maths the operation performed by this module is known as Heaviside function Photo
103. t for Circular Activation Space Plot for Elliptic activation G Circular Activation states 0 75 excin infiln 0 5 modin vm 0 25 act i live data amp D 5 Figure 48 Circular and elliptic activation e Now repeat the exercise trying to get a Gaussian activation of the post synaptic neuron group Again modify the arborization properties Projective Field of the synapse and the attenuation You should get a space plot similar to Figure 49 The 3 d view of this activation should be similar to Figure 50 Q1 Write values in the table J 10 20 0 Ss ID 15 20 25 Figure 49 Gaussian activation Linear Threshold Gaussian Arb 075 states excin P infin 0 3 p modin Wri 0 25 act Ww live data Gaussian distribution 3D Figure 50 Example in 3d of a gaussian distribution e OO o y o o Artois OOOO CS o Sna O l o i A A A e Stop the simulation Create an inhibitory connection from the Integrate and Fire group to the Linear Threshold one using the blue arrow icon and set the synapse type to Uniform fixed weight this means that every connection between the pre synaptic neuron group and the post synaptic neuron has the same weight gain Run the simulation again Q1 What do you see Q2 Which is the difference between an excitatory and inhibitory synapse Check the membrane potential state using the time plot to verify what is happening e Now we want the Line
104. t on the depression of the parallel fibres synapses in the cerebellar cortex 18 19 and it includes several assumptions The representation of time intervals between conditioned and unconditioned stimulus events is assumed to depend on intrinsic properties of the parallel fibre synapses spectral timing hypothesis The model also integrates the Cerebellar Memory Consolidation and Savings 323 idea that cerebellar cortex deep nucleus and the inferior olive form a negative feedback loop which controls and stabilizes cerebellar learning 20 A schematic representation of the model components and connections and its anatomical counterparts is illustrated in Fig 1 of cf cs CR US Fig 1 The cerebellar circuitry CS and US inputs converge on a Purkinje cell which is composed of three compartments PuSy PuSp and PuSo referring respectively to the dendritic region the spontaneous activity and the soma of the Purkinje cell The inferior olive IO Purkinje cell and deep nucleus AIN form a negative feedback loop The CS indirect pathway conveys the CS from the Pontine nuclei Po through the mossy fibres mf the granular layer Gr and the parallel fibres pf to the Purkinje cell A CS direct pathway conveys the CS signal from Po to the AIN through the collaterals of the mossy fibres mfc The US signal is conveyed to the Purkinje cell through the climbing fibres cf generating from IO The inhibitory inter neurons I re
105. the changes e Change the neuron type of the second group to Integrate and Fire Set the right threshold value in order to obtain the negative version of result of Tutorial 4 Provide screen shots of the result and explain how you did it an zE PE iaio Appendices Ulysses Bernardet 14 Appendix I Neuron types This section of the Appendix gives an overview of the neuron types most frequently used in iqr Input types Depending on the connection type neurons in the post synaptic group will receive either e excitatory excln inhibitory inhIn or e modulatory modIn input Attention Not all types of neurons make use of all three types of inputs 14 1 Random spike A random spike cell produces random spiking activity with a user defined spiking probability Unlike the other cell types it receives no input and has no membrane potential The output of a random spike cell at time t 1 a t 1 is given by SpikeAmpl with bability Prob ad eB peerinpl wi pi lity 0 otherwise e Parameters name Probability Prob description Probability of a spike occurring during a single time step range 0 0 1 0 name Spike amplitude SpikeAmpl description Amplitude of each spike range 0 0 1 0 e States name Activity act description 14 2 Linear threshold Graded potential cells are modelled using linear threshold cells The membrane potential of a linear threshold cell at time t 1 v t 1 is
106. the connection e Set the pattern map to all instead of center e Once you have changed that press Apply and Accept If apply is not pressed changes will not be stored e Click right button in the connection and select connection plot It gives you a the idea of how each neuron is connected to the other one This is a useful tool that you may want to use every time you create a new connection to check if the mapping you want is correctly set a 7 ep i CUa Uye Diophus Bucur afigr Docs swr WA Ta z 23 r fe tl Fb OL TE E Properties for Connection Conn E PA ch oc e be h a 0 r Marne Properties Notza Now Sys EN P Tut 2 Process 4 new system Cunnecliun Ware onSsoke Group 2Lreathresheld Connection type excitato y Connections Tut 2 Procece Tatern current type PabtenMapped edit Co n nectivity zet type FattemMapoed patte m Arbarize tien current type ArbRect edit set type ArbRect Attenuation unction current trpe FunUniforrr edit sel Lype FurUrilan z Delay unctor wurrenil Lype FunUniforrs edil cet type FunUriform ia Te Axed welght edit Ficed Ae x 3E Weight function cLITent type cet type Figure 46 Excitatory connection scheme and Connection properties dialog e Play with it Click in both squares that represent the neuron groups Arrow sh
107. the pf Pu synapse black trace and mf AlIN grey trace synapse over the entire sessions ACQUISITION ob EXTINCTION RE ACQUISITION 100 im 100 80 Bly an a 90 B0 fi al 2 40 40 Al 20 2 20 2 4 6 a 10 4 ii t i 2 4 j i 10 Blocies of triats Blocks of trials Blocks of trials on d e janie CS CR 120 120 2 C5 US 100 100 100 i Bo Interval tS f Ba 30i Fig 4 Learning performance of the robot during the obstacle avoidance task Top Effective CRs per blocks of 10 CSs Bottom changes in the inter stimulus interval CS US triangles and CR latency CS CR circles for the 300 CSs occurred during an experiment Net LTD effects extinction in the nuclear synapse are then only visible when the nuclei are strongly inhibited by the recovered Purkinje cell activity The retention of a residual nuclear plasticity is dependent on the small LTD ratio chosen to reverse the effects of the long term potentiation This residual plasticity in the mf AIN synapse is what actually leads to a faster expression of the CRs in the reacquisition session as shown in Fig 4 The results of this simulation illustrate that due to its increased excitability less plasticity in the cortex is necessary to induce a rebound in the A N and consequently express a robust CR The magnitude of observable savings is therefore dependent on the amount of residual plasticity in the nuclei Longer extinction training can completely rever
108. to read states from neurons and feed data into states of neurons The functions provided for this purpose are using a naming schema that is module centered data that is read from the group is referred to as input from group data fed into a group is pointed to as output to group The references for theses states will be set in the module properties of the process see iqr Manual Defining the output to a group is done with the addOutputToGroup function ClsStateVariable outputStateVariable GUEDUE Stabe Variabilke addOnurourloGroup ouEDUEO identirtier NOutpurt 0 description desceriptions g StateArray amp clsStateArrayOut outputStateVariable gt getStateArray Specifying input from a group into a module employs a slightly different syntax using the StateVariablePtr class StateVariablePtr inputStateVariablePtr inpurcStateYariablePte addin jue romero input O identirtier Taput 0 description descriprcion s StateArray amp clsStateArrayInput inputStateVariablePtr gt getTarget gt getStateArray Once the StateArray references are created the data can be manipulated as described above Please do not write into the input from group StateArray The result might be catastrophic When adding output to groups or input from groups no size constraint for the state array can be defined It is therefore advisable to either write the module in a way that it can cope with arbitra
109. to the Khepera K Team_ S A http www k team com Lausanne Switzerland and e puck mobile robot http www e puck org More information about the robots can be found in the e puck Mini Doc http www gctronic com files miniDocWeb png and the Khepera User Manual http ftp k team com khepera documentation KheperaUserManual png Top view Side view a Figure 54 Schematic drawing for the Khepera robot from Khepera User Manual 1 LEDs 2 Serial line connector 3 Reset button 4 Jumpers for the running mode selection 5 Infra Red sensors 6 Battery recharge connector 7 ON OFF battery switch 8 Second reset button a Khepera 6 e puck Figure 55 Arrangement of infra red sensors A full description of the features of the robots can be found in the respective manuals here only the most important features are described Communication The communication with the computer is done over a serial cable Khepera or via BlueTooth E puck e Parameters name Serial Port description Path to Serial Port Under Linux this will be for example if connected via a cable dev ttyO or def rfcomm0O if connected via Blue Tooth Under Windows this will be for example COM20 default Knepera dev ttyS0O E puck dev rfcomm0 name Show output description Show Khepera Display options true false name Frontal LED on description Turn Frontal LED on options true false name Lateral LED on
110. topology Random Spike Neuron amp properties probability amplitude Cycles Per Second CPS Space plot Time plot amp Sync plots e Create a simulation containing a group of cells that spike randomly e Run the simulation and see the output using Space Plots and Time Plots e Build your first igr system 8 2 Advice e Go slowly e It is important that you understand what you are doing Try to assimilate the concepts group of neurons process synapses time plot CPS arborization etc when their appear Otherwise you will be lost for future exercises e Keep a copy of the igr User Manual handy and make sure you read the corresponding section in the manual when new concepts are introduced Checking each concept there will save a lot of time e It is also important that after the practicum you create back ups of every file e Don t forget to save your simulation after you make modifications File Save As and give ita name like Tutorial 1 8 3 Building the System e First of all you have to create a process using the process button in the button bar This will happen at the system level To do this press the process P button and then move the mouse to the diagram editing panel and press left button again A grey square New Process 1 should have been created Figure 42 e Once the process is created you edit its name by editing its properties Change the name to Tut 1 Process A new tab appea
111. trate of the cerebellar engram In order to test this hypothesis we have presented a computational model of the cerebellar circuit that we explored using both simulated conditioning trials and robot experiments We observed that the model is able to acquire and maintain a partial memory of the associative CR through the cooperation of the cerebellar cortex and the cerebellar nuclei The results show how nuclear plasticity can induce an increase in the general excitability of the nuclei resulting in a higher facilitation of rebound when released from Purkinje cell inhibition Nevertheless the minimum numbers of trials necessary to produce a CR are still dependent on the induction of plasticity in the cortex A limit of the model is that learning parameters in the two sites of plasticity need to be tuned in order to obtain a stable behaviour and prevent a drift of the synaptic weights Moreover given no direct evidence of a slower learning rate in the nuclear plasticity we based our assumption on the evidence that following extinction short latency responses have been observed in rabbits after disconnection of the cerebellar cortex 24 Savings have been shown to be a very strong phenomena and normally very few paired trials are sufficient to induce again a CR 2 Our model showed that part of the memory can be copied to the nuclei however this does not exclude that other mechanisms dependent on alternative parts of the cerebellar circuit could comple
112. ues as well 256 is an important number for computers In some situation though we may be interested in reducing all this amount of information 256 or more different values to just two O and 1 And with O and 1 we may be encoding dark or clear too close or far away noisy or silent etc This is one of the easiest operation that we can apply to the data given by a sensor it consists in setting a threshold and consider that only values above that threshold correspond to the active class or 1 12 2 Example Discrimination between classes of spots in an image Let s think of a task where we have to search for a white object in an image for instance a football or a pale face If we are provided with a monochrome camera a first step towards finding a white ball could be to discriminate clear spots form darker ones We can do this by setting a threshold Quite likely the main object that your webcam will find in its visual field is your face With such working material one of the games you can play is to search for the threshold value that better isolates your face from the background Based on luminance gray level this will be an easy task if for instance your skin color is clearer than the background if your skin color is darker that the background you have to jump to the Negative Image exercise 12 3 Implementation Processes To implement this operation in IQR we only need one process with its type set to video module
113. uisition is due to the collaboration of the two sites of plasticity implemented in the model The paired presentation of CSs and USs gradually depresses the efficacy of the pfPu synapses black line in Fig 3d and in turns it leads to a pause in PuSo not shown Potentiation at the nuclear site is only possible after the inhibition from PuSo starts to be revoked The appearance of the first CR is then critically dependent on the level of membrane potential excitability of the A N that can subsequently repolarize Following acquisition training the circuit without the reinforcing US leads to extinction of the acquired CRs Fig 3b This is primarily due to the reversal effect of LTP in the pfPu synapse Fig 3e which gradually restores the activity in the Purkinje cell during the CS period A partial transfer of the memory will then still occur during the first part of the extinction training until the Purkinje cell activity is not completely recovered ACQUISITION s EXTING TION b RE ACQUISITION eal B1 40 CRs frequency ol i 10 2 A t a 10 2 S R a 10 Blocks di riala Bloks Ws ag Ja jZ a 1 a fad 7 ij i i Times a x 10 aed ce Timestens Fig 3 Performance of the circuit during simulated acquisition extinction and reacquisition Top Learning curves for experiments with an inter stimulus interval of 300 ts The percentage of CRs is plotted over ten blocks of ten trials Bottom row Synaptic weights changes induced by plasticity at
114. us will be defined in two dimensions as shown in Figure 14 source flow of information synapse dendrite target Figure 14 Two dimensional nexus Delays The layout shown in Figure 13 is directly derived from the above listed assumptions that delays are properties of dendrites The basis of the computation of the delay is the distance as given by the eccentricity of a neuron In the case of a receptive field the eccentricity is defined by the position of the sending cell relative to the position of the one receiving cell In Figure 13 a this definition of the distance and the resulting values are depicted In a projective field the eccentricity is defined with respect of the position of the multiple post synaptic cells relative to the one pre synaptic neuron Figure 13 b 4 17 1 Patterns and arborizations The next step is to understand how the connectivity can be specified in iqr Defining the connectivity comprises of two steps e define the pattern e define the arborization Pattern The Pattern defines pairs of points in the lattice of the pre and post synaptic group These points are not neurons but x y coordinates In Figure 15 the points in the pre synaptic group are indicated by the arrow labeled projected point For sake of ease of illustration we ll use a one dimensional layout again The groups are defined with size 10x1 for source and 2x1 for target The two pairs of points are
115. ve to be the same 16 2 3 Lego MindStorm Operating system Linux This module provides an interface to the Robotics Invention System 1 0 from Lego MindStorms It is used for online control of the motors and sensors The hardware consists of a serial infra red transmitter the RCX microcomputer brick motors and sensors for touch light and rotation The serial infra red transmitter is connected to the serial port of the computer The RCX microcomputer brick receives information from the transmitter and interfaces to motors and sensors Figure 58 IR interface Sensor interface i light sensor y f touch sensor touch sensor display Figure 58 RCX Interface brick for Lego MindStorm LEGO Company e Parameters name SensorMode description Mode for each sensor options set raw boolean name SensorType description Type for each sensor options raw touch temp light rot name Serial Port description Path to Serial Port default dev ttySO e States name Sensors direction Module Group description Sensor readings size 3 name Motor Input direction Group Module description Motor power The used range is 2 0 lt 1 size 6 even number required name Float Input direction Group Module description A value gt 0 sets the motor to zero resistance mode size 3 name Flip Input direction Group Module description A value gt 0 changes the directio
116. wFrocess amp New Pocess 2 Ognegl oriz ES s Maw Erocess Ccpy al Blew iq Harbor zjn x Typa Met ID lin a ele Mi Wlas Value 1G CUp Mew OLE 1 1 S341 PUNSO KET TESS ci Random spike Presani ty i 1 Oji Frobakli ty Caicel Apply De ete Rowo Refresh Leas ceg Save Comlyg Load Peranste s Save Parametere Figure 35 Using the Harbor users can collect system elements such as parameters of neurons and synapses in a central place Items are added to the Harbor by dragging them form the Browser Harbor configurations can be saved and loaded Remote control of iqr In a number of use cases being able to control a simulation from outside the simulation software itself is useful For this purpose iqr is listening to incoming message on a user defined TCP IP port This allows to control the simulation and change parameters of the system Concretely this remote control interface supports the following syntax cmd lt COMMAND gt itemType lt TYPE gt itemName lt ITEM NAMFE gt itemID lt ITEM NAME gt paramID lt ID gt value lt VALUE gt The supported COMMAND are start stop quit param startsampler stopsampler The param command allows to change the parameter of elements and needs as an argument the type of item ItemType PROCESS GROUP NEURON CONNECTION SYNAPSE the name itemName or ID itemID of the element the ID of the parameter P
117. we want to build systems able to generate a meaningful behaviour in the real world they have to be complete in the sense that they must spawn from sensory processing to the behavioural output Systems compliant with this requirement will inevitably be of a large scale where the overall architecture is of critical importance An human brain consists of about 80 billion neurons Neurons in turn are vastly outnumbered by synapses the estimations of the numbers of synapses per average neuron ranging from 1 000 to 10 00 This impressive ratio of number of neurons vs number of synapses speaks in favour of the view that biological neuronal networks draw their computational power from the large scale integration of information in the connectivity between neurons The second cornerstone of iqrs design philosophy therefore is the support for large scale neuronal architecture and sophisticated connectivity 2 What igr is iqr is a tool for creating and running simulations of large scale neural networks The key features are graphical interface for designing neuronal models e graphical on line control of the simulation e change of model parameters at run time on line visualization and analysis of data e the possibility to connect neural models to real world devices such as cameras mobile robots etc e predefined interfaces to robots cameras and other hardware open architecture for writing own neuron synapse types and interfa
118. wi t otherwise The learning rate constants and n are set to allow one strong LTD event as the result of simultaneous cf and pf activation and several weak LTP events following a pf input As a result pf stimulation alone leads to a weak net increase of pf Pu synapse while pf stimulation followed by cf stimulation leads to a large net decrease A second mechanism of plasticity is implemented at the mfA N synapse Contrarily to the well established induction of LTP and LTD at pf Pu synapse evidence for a potentiation of the mossy fibres collaterals to deep nucleus synapses has been sparse LTP and LTD effects of this synapses in vitro 23 15 16 as well as increases in the intrinsic excitability of the A N 21 have been shown Theoretical studies 24 also suggest that LTP in the nuclei should be more effective when driven by Purkinje cells Hence in our model the induction of LTP depends on the expression of a rebound current r in the A N after release from PuSo inhibition and the coincident activation of mf s while LTD depends on the mfs activation in the presence of inhibition The weights at this synapses are updated according to the following rule wij t a wit wij t if Ame t gt 0 and 6 LTP wij t 1 4 w t B wig wiz t if Ame t gt O LTD Wy t otherwise The learning rate constants at mfAI N synapse are chosen in order to obtain a slower potentiation and depression than at the pf Pu syna

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