Neural transformations on spike timing information (Tripp and Eliasmith 2007)

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Accession:136380
" ... Here we employ computational methods to show that an ensemble of neurons firing at a constant mean rate can induce arbitrarily chosen temporal current patterns in postsynaptic cells. ..."
Reference:
1 . Tripp B, Eliasmith C (2007) Neural populations can induce reliable postsynaptic currents without observable spike rate changes or precise spike timing. Cereb Cortex 17:1830-40 [PubMed]
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Temporal Pattern Generation;
Implementer(s): Tripp, Bryan [bryan.tripp at mail.mcgill.ca]; Eliasmith, Chris [celiasmith at uwaterloo.ca];
% This script experiments with learning

clear all

dt = .0002;
x = load('signals_figure4.mat');
signal = x.signals(2,:);

[spikes, cov] = genUncorrelated(500, .3, dt, 30, [1 0 0]);
[statspikes, cov] = genUncorrelated(20, 10, dt, 200, [0 1 0], struct('SD', .001, 'meanSD', .002));
mean(cov)
std(cov)

iterations = 1000;

[weights, error] = learnedDecoders(signal, spikes, zeros(500,1), iterations, 0, 0);
[weightsJitter, errorJitter] = learnedDecoders(signal, spikes, zeros(500,1), iterations, 0.004, 0);
[weightsFilter50, errorFilter50] = learnedDecoders(signal, spikes, zeros(500,1), iterations, 0, 0.05);
[weightsFilter500, errorFilter500] = learnedDecoders(signal, spikes, zeros(500,1), iterations, 0, 0.5);
 
[weightsFilterJitter, errorFilterJitter] = learnedDecoders(signal, spikes, zeros(500,1), iterations, 0.004, 0.05);
save 'data_learning.mat';

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