Transcript See the tutorial (network)

GUM*02 tutorial session
UTSA, 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


Networks implement computations


network of networks
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
model
real
Physiology: inhibitory neurons
inputs
spike rasters
ISI distribution
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


most messages sent each time step


field values
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 /neuron1
create compartment /neuron1/soma
setfield ^ \
Em
{ Erest }
\
Rm
{ RM / area } \
Cm
{ CM * area } \
Ra
{ RA * len / xarea }
//
//
//
//
volts
Ohms
Farads
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 a-function when spike hits
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
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
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!
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]
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
\
\
\
\
\
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!