GUM*02 tutorial session UTSA, San Antonio, Texas
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Transcript of GUM*02 tutorial session UTSA, San Antonio, Texas
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!