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

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

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

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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

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

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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?

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

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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?

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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

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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

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

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Neuron types

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Simplification

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Physiology: pyramidal neurons real model

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Physiology: inhibitory neurons

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inputs ISI distributionspike rasters

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Connectivities 1 afferents

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Connectivities 2

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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

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results? see my talk tomorrow hint: I graduated

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

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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]

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

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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

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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

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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

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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

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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!

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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]

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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

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

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

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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

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

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

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