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1CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Neural Simulation LanguageNSL
2CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Overview
IntroductionNSLMExample (Max Selector)NSLSDownloading and installing NSL
3CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Introduction
NSL is a platform for
Building neural architectures (modeling) NSLM
NSLJ & NSLCExecuting them (simulation).
NSLS
NSL provides tools for modeling complex neural systems - especially (but not only) when the neurons are modeled as leaky integrator neurons.
4CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Methodology
The general methodology for making a complex neural model of brain function is to combine different modules corresponding to different brain regions.
To model a particular brain region, we divide it anatomically or physiologically into different neural arrays.
Each brain region is then modeled as a set of neuron arrays, where each neuron is described for example by the leaky integrator, a single-compartment model of membrane potential and firing rate.
5CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Levels of abstraction
A complete model in NSL requires the following components:
•a set of modules defining the entire model
•neurons comprised in each neural module
•neural interconnections
•neural dynamics
•numerical methods to solve the differential equations.
6CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Example of modules
Bischoff's Model
Retina
Basal Ganglia
Hoff - Arbib
Thalamus
VLO
Cortex
SMA
MC
PreSMA SMAProper
Inh Mvt
Putamen
GPi/SNr
SNc
GPe
STN
7CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Leaky integrator
8CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Simulation
1
2
3 4
5
6
7
9CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Simulation Methods
initSysinitModule
makeConn … (simulation steps)endModule
endSys
10CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Simulation Methods
initTrainEpoch
initTrain
simTrain
endTrain
endTrainEpoch
initRunEpoch
initRun
simRun
endRun
endRunEpoch
train
run
trainAndRunAlldoTrainEpochTimes
doRunEpochTimes
11CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Model Structures
Model Highest level
Modules NeuralNetworks
InModulesOutModules
Graphic Interfaces
MotorModules Robotics
NslClass Libraries New Canvases New NSLS Commands
12CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
NSLM Types
Primitive typesintfloatdoublebooleanchar
NslData types (0, 1, 2, 3, 4)NslIntNslFloatNslDoubleNslBooleanNslString (0)
Could be public, private or protected.
NslPort types (0, 1, 2, 3, 4)NslDinIntNslDinFloatNslDinDoubleNslDinBooleanNslDinString (0)NslDoutIntNslDoutFloatNslDoutDoubleNslDoutBooleanNslDoutString (0)
Ports must be public
13CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Max Selector Model
The details of this model can be found in section 4.4 of TMB2.
The model uses competition mechanisms to obtain, in many cases, a single winner in the network where the input signal with the greatest strength is propagated along to the output of the network.
14CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Max Selector Model (2)
15CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Max Selector Model (3)
MaxSelectorModel
MaxSelectorStimulus Output
ULayer
VLayer
16CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
MaxSelectorModel
nslImport nslAllImports;
nslImport MaxSelectorStimulus;nslImport MaxSelector;nslImport MaxSelectorOutput;
nslModel MaxSelectorModel() {
nslConst int size = 10;
private MaxSelectorStimulus stimulus(size); private MaxSelector maxselector(size); private MaxSelectorOutput output(size);
public void initSys() {system.setRunEndTime(10.0);system.nslSetRunDelta(0.1);
}
public void makeConn() { nslConnect(stimulus.s_out, maxselector.in);
nslConnect(stimulus.s_out, output.s_out);nslConnect(maxselector.out, output.uf);
}}
17CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
MaxSelectorStimulus
nslImport nslAllImports;
nslModule MaxSelectorStimulus(int size) {
public NslDoutDouble1 s_out(size);
public void initRun() {s_out=0;s_out[1]=0.5;s_out[3]=1.0;
}}
18CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
MaxSelectorOutput
nslImport nslAllImports;
nslOutModule MaxSelectorOutput(int size) { public NslDinDouble1 s_out(size); public NslDinDouble1 uf(size); private NslDouble1 up(size); private boolean worked= false; public void initModule() {
up.nslSetAccess('W'); nslAddAreaCanvas(s_out,0,1); nslAddTemporalCanvas(up,-2.5,2.5); nslAddAreaCanvas(uf,0,1); }
public void simRun() { worked=nslSetValue(up,"maxSelectorModel.maxselector.u1.up"); }}
19CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
MaxSelector
nslImport nslAllImports;
nslImport Ulayer;nslImport Vlayer;
nslModule MaxSelector(int size) {
public NslDinDouble1 in(size); public NslDoutDouble1 out(size);
private Ulayer u1(size); private Vlayer v1();
public void makeConn() {nslRelabel(this.in, u1.s_in);nslConnect(u1.uf, v1.u_in);nslConnect(v1.vf, u1.v_in);nslRelabel(u1.uf, this.out);
}}
20CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
ULayer
nslImport nslAllImports;
nslModule Ulayer(int size) { //inports public NslDinDouble1 s_in(); public NslDinDouble0 v_in(); //outports public NslDoutDouble1 uf(size); //variables private NslDouble1 up(size); private NslDouble0 w1();
private NslDouble0 w2(); private NslDouble0 h1(); private NslDouble0 k(); private double tau;
…
21CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Ulayer(2)
public void initRun(){
uf = 0;up = 0;tau = 1.0;w1= 1.0;w2= 1.0;h1= 0.1;k= 0.1;
}
public void simRun(){//compute : up=up+((timestep/tu)*du/dt)up = nslDiff(up, tau, -up + w1*uf-w2*v_in - h1 + s_in);uf = nslStep(up,k.get(),0,1.0);
}}
22CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
VLayer
nslImport nslAllImports;
nslModule Vlayer() {
// ports public NslDinDouble1 u_in();
// output port public NslDoutDouble0 vf(); // variables private NslDouble0 vp(); // neuron potential private NslDouble0 h2(); private double tau; // time constant
…
23CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Vlayer (2)
public void initRun() {vf=0;vp=0;tau=1.0;h2 = 0.5;
}
public void simRun() {// vp=vp+((timestep/tv)*dv/dt)vp = nslDiff(vp, tau, -vp + nslSum(u_in) -h2);vf = nslRamp(vp);
}}
24CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Compilation
One model/module per fileThe file extension must be .modWe recommend to clean the model directory before compiling with the nslclean command
To compile the model you just have to execute the following command: nslc modelName
Where modelName is the Name of the file that contains the model structure.
For this example we should write: nslc MaxSelectorModelNote that we didn’t write the file extension at the end of the name.
Mod File Nlx File Java File Class File
25CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Execution
To simulate your model you have to use the nsl command.
For this example you should write: nsl MaxSelectorModel
Two running modesText (-nodisplay)Graphic interface (default)
To redirect the standard output (-stdout console)To redirect the standard error (-stderr console)
26CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Interface
27CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Interface (2)
28CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
NSLS
To avoid re-compiling every time you modify your model parameters we provide the NSL script language known as NSLS which also provides a dynamic user control environment.
NSLS provides the following functionality:NSL model parameter assignmentNSL input specificationNSL simulation controlNSL file controlNSL graphics control
NSLS is an extension of the well know TCL scripting language, thus providing NSL and TCL functionality.
29CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
NSLS (2)
NSL command syntax: nsl subcommand [options]
Important NSL commands:nsl source fileName
(i.e. nsl source hopfield.nsls)nsl set variable value
(i.e. nsl set maxSelectorModel.stimulus.s_out {1 0 0.5})nsl get variable
(i.e. nsl get maxSelectorModel.stimulus.s_out)nsl runnsl train…nsl exit
30CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
NSLS example
## Hopfield Network#
set A {}set B {}set C {}set D {}
proc memorize { x } {
puts "Memorizing $x" nsl set hopfield.inModule.input $xnsl train
}
proc test { x d } {
nsl set hopfield.dis $d nsl set hopfield.inModule.input $xnsl run
}
31CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
NSLS example (2)
proc initData {} {
global A B C D
set A { { -1 -1 1 1 -1 -1 }
{ -1 1 -1 -1 1 -1 } { -1 1 1 1 1 -1 } { -1 1 1 1 1 -1 }{ -1 1 -1 -1 1 -1 }{ -1 1 -1 -1 1 -1 }
} set B {
{ 1 1 -1 -1 -1 -1 }{ 1 1 -1 -1 -1 -1 } { 1 1 1 1 -1 -1 }{ 1 1 -1 -1 1 -1 } { 1 1 -1 -1 1 -1 } { 1 1 1 1 -1 -1 }
} … }
32CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
NSLS example (3)
proc trainNetwork {} {global A B C D
memorize $Amemorize $Bmemorize $Cmemorize $D
}
proc NslMain {} {global A B C Dputs "Initializing"initDataputs "Training"trainNetworkputs "Testing"for { set i 10 } { $i<20 } { incr i } {
puts "Testing with distortion $i"test $A $i
}nsl set hopfield.dis 0
}
NslMain
33CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Downloading NSL
First, you will need to install the latest Java SDK; get it directly from Sun at http://java.sun.com/j2se/1.3/.
Once this is setup and working, download the entire NSL tree from http://www-scf.usc.edu/~csci564/nsl.tar.gz
Extract the archive (Winzip or Pkzip).
Edit the file "NSL3_0_n\resume.bat" such that it matches your environment (you will have to specify the path where you installed Java, where you installed NSL, etc).
Execute the resume batch file before beginning a NSL session.
34CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
Downloading NSL (2)
@echo offecho Initializing NSL environment variables
set NSLJ_ROOT=C:\salvador\NSL3_0_nset JAVA_HOME=C:\jdk1.3set NSL_OS=windows
echo Updating path and classpath
set PATH=%JAVA_HOME%\bin;%NSLJ_ROOT%;%PATH%set CLASSPATH=%NSLJ_ROOT%;.;%NSLJ_ROOT%\nslj\src\main; %NSLJ_ROOT%\nslj\src\nsls\jacl; %NSLJ_ROOT%\nslj\src\nsls\tcljava
@echo on
35CS564 - Brain Theory and Artificial Intelligence, USC, Fall 2001.
References
A Weitzenfeld, MA Arbib and A Alexander, 2002, NSL Neural Simulation Language, MIT Press (in press)
An old version is at:
http://www-hbp.usc.edu/_Documentation/NSL/Book/TOC.htm
For any NSL related questions and bug reports, please send me an email at [email protected]