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GUM*02 tutorial session UTSA, San Antonio, Texas Large-scale realistic modeling of neuronal networks Mike Vanier, Caltech.

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Presentation on theme: "GUM*02 tutorial session UTSA, San Antonio, Texas Large-scale realistic modeling of neuronal networks Mike Vanier, Caltech."— Presentation transcript:

1 GUM*02 tutorial session UTSA, San Antonio, Texas Large-scale realistic modeling of neuronal networks Mike Vanier, Caltech

2 Structure of the talk: General network modeling issues Details of how networks are modeled in GENESIS

3 Part 1 General network modeling issues Details of how networks are modeled in GENESIS

4 Why model networks? Goal: understand the brain network of networks Networks implement computations influence of NN theory Networks are where the action is!

5 Why avoid modeling networks? networks are too complex dozens of cell types complex connectivities, interactions we don’t understand neurons yet not enough data want to graduate quickly

6 Roots of GENESIS GENESIS: GEneral NEural SImulation System network modeling was orig focus

7 and yet... most models still either single neuron models very small networks “abstract” network models maybe a 10:1 ratio or worse why is this?

8 Network modeling is hard!!! need accurate data on: neuron models (ALL types) connectivities inputs outputs simplifications needed scaling issues

9 More typical scenario data available for some neurons only inhibitory neurons? connectivities only vaguely known inputs vaguely known if at all outputs vaguely known if at all why bother?

10 Motivations “ Abandon all hope, ye who enter here.” more exploratory, less definitive refine conceptual model of system make implicit ideas about function explicit figure out what data to collect

11 The process collect all the data you can!!! build simplified neuron models match to data build model of inputs build network model match to data graduate

12 Example: piriform cortex neuron types well established little physiology for most connection patterns known inputs partially known outputs mostly unknown

13 Neuron types

14 Simplification

15 Physiology: pyramidal neurons real model

16 Physiology: inhibitory neurons

17 inputs ISI distributionspike rasters

18 Connectivities 1 afferents

19 Connectivities 2

20 now the “fun” begins... pick network phenomenon to model PC: response to strong, weak shocks independent of details of bulb relatively simple adjust parameters to tune model leave neuron parameters alone connectivities

21 results? see my talk tomorrow hint: I graduated

22 Part 2 General network modeling issues Details of how networks are modeled in GENESIS

23 GENESIS basics modeler creates simulation objects objects send messages to ea. other messages contain data field values most messages sent each time step or once per fixed interval [spikes break this rule]

24 neurons compartmental models of neurons neuron composed of compartments compartments are isopotential channels connect to compartments voltage-dependent calcium-dependent synaptic

25 setting up the neuron create neutral /neuron1 create compartment /neuron1/soma setfield ^ \ Em { Erest } \ // volts Rm { RM / area } \ // Ohms Cm { CM * area } \ // Farads Ra { RA * len / xarea } // Ohms

26 spikes in genesis spikegen object monitors V m of compartment when past threshold, sends SPIKE message to destination synchan object receives SPIKE message stores time of spike in buffer generates  -function when spike hits

27 setting up the synchan create synchan /neuron1/syn setfield ^ \ gmax 1.0e-9 \ // 1 nS Ek 0.0 \ tau1 0.001 \ // rise time (sec) tau2 0.003 // fall time // Connect soma to synchan: addmsg /neuron1/soma /neuron1/syn VOLTAGE Vm addmsg /neuron1/syn /neuron1/soma CHANNEL Gk Ek

28 setting up the spikegen // Create and connect spike detector: create spikegen /neuron1/spike setfield ^ thresh -0.020 abs_refract 0.002 addmsg /neuron1/soma /neuron1/spike INPUT Vm

29 connecting two neurons // Assume we have neuron2 like neuron1 addmsg /neuron1/spike /neuron2/syn SPIKE // Set synaptic weight and delay: setfield /neuron2/syn \ synapse[0].weight 1.0 \ synapse[0].delay 0.001 // 1 msec // That’s all there is to it!

30 building networks Why not just do this for all synapses? 100-1000 neurons, 10,000-100,000 synapses... gets pretty tedious faster way: large-scale connection commands volumeconnect [planarconnect] volumedelay [planardelay] volumeweight [planarweight]

31 volumeconnect volumeconnect source_elements destination_elements \ -relative \ -sourcemask {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -sourcehole {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -destmask {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -desthole {box, ellipsoid} x1 y1 z1 x2 y2 z2 \ -probability p

32 volumedelay volumedelay sourcepath [destination_path] \ -fixed delay \ -radial conduction_velocity \ -add \ -uniform scale \ -gaussian stdev maxdev \ -exponential mid max \ -absoluterandom

33 volumeweight volumeweight sourcepath [destination_path] \ -fixed weight \ -decay decay_rate max_weight min_weight \ -uniform scale \ -gaussian stdev maxdev \ -exponential mid max \ -absoluterandom

34 note on connection commands mainly useful for simple cases more realistic cases require more control GENESIS script language makes it easy to write own connection commands

35 output Xodus graphical output dump neuron data to files binary files readable by “xview”

36 conclusions network modeling is fun fascinating fundamental frustrating! NOT for the easily discouraged!


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