GUM*02 tutorial session UTSA, San Antonio, Texas

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GUM02 tutorial sessionUTSA, 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 dont 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 101 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
model
real
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Physiology inhibitory neurons
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inputs
ISI distribution
spike 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 Vm of compartment
• when past threshold, sends SPIKE message to
  destination
• synchan object
• receives SPIKE message
• stores time of spike in buffer
• generates a-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 \
• synapse0.weight 1.0 \
• synapse0.delay 0.001 // 1 msec
• // Thats 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!