GUM*02 tutorial sessionUTSA, San Antonio, Texas

Large-scale realistic modeling of neuronal

networks

Mike Vanier, Caltech

Structure of the talk:

General network modeling issues

Details of how networks are modeled in GENESIS

Part 1

General network modeling issues

Details of how networks are modeled in GENESIS

Why model networks? Goal: understand the brain

network of networks Networks implement

computations influence of NN theory

Networks are where the action is!

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

Roots of GENESIS GENESIS:

GEneral NEural SImulation System

network modeling was orig focus

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?

Network modeling is hard!!! need accurate data on:

neuron models (ALL types) connectivities inputs outputs

simplifications needed scaling issues

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?

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

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

Example: piriform cortex

neuron types well established little physiology for most

connection patterns known inputs partially known outputs mostly unknown

Neuron types

Simplification

Physiology: pyramidal neurons

realmodel

Physiology: inhibitory neurons

inputsISI distributionspike rasters

Connectivities 1

afferents

Connectivities 2

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

results?

see my talk tomorrow hint: I graduated

Part 2

General network modeling issues

Details of how networks are modeled in GENESIS

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]

neurons compartmental models of neurons

neuron composed of compartments compartments are isopotential channels connect to compartments

voltage-dependent calcium-dependent synaptic

setting up the neuron

create neutral /neuron1create compartment /neuron1/somasetfield ^ \ Em { Erest } \ // volts Rm { RM / area } \ // Ohms Cm { CM * area } \ // Farads Ra { RA * len / xarea } // Ohms

spikes in genesis spikegen object

monitors Vm 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

setting up the synchancreate synchan /neuron1/synsetfield ^ \ 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 Vmaddmsg /neuron1/syn /neuron1/soma CHANNEL Gk Ek

setting up the spikegen

// Create and connect spike detector:create spikegen /neuron1/spikesetfield ^ thresh -0.020 abs_refract 0.002addmsg /neuron1/soma /neuron1/spike INPUT Vm

connecting two neurons

// Assume we have neuron2 like neuron1addmsg /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!

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]

volumeconnectvolumeconnect 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

volumedelay

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

volumeweight

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

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

output

Xodus graphical output

dump neuron data to files binary files readable by “xview”

conclusions network modeling is

fun fascinating fundamental frustrating!

NOT for the easily discouraged!