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GUM*02 tutorial session UTSA, San Antonio, Texas

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GUM*02 tutorial session. UTSA, San Antonio, Texas. Large-scale realistic modeling of neuronal networks ... Details of how networks are modeled in GENESIS. Part ... – PowerPoint PPT presentation

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Title: GUM*02 tutorial session UTSA, San Antonio, Texas


1
GUM02 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 dont 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 101 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
model
real
16
Physiology inhibitory neurons
17
inputs
ISI distribution
spike 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 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

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 \
  • synapse0.weight 1.0 \
  • synapse0.delay 0.001 // 1 msec
  • // Thats 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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