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

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" ... 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. ..."
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;
Gap Junctions:
Simulation Environment: MATLAB;
Model Concept(s): Activity Patterns; Temporal Pattern Generation;
Implementer(s): Tripp, Bryan [bryan.tripp at]; Eliasmith, Chris [celiasmith at];
% Script to test that type-I error is roughly maintained with Poisson 
% variables instead of normal variables (within ranges of interest)

clear all 

bins = 200;
lambda = .1;
ne = 0;
alpha = .05;

trials = [10 31 100 315 1000 3150]; %make sure it works over a wide range of #s of trials per experiment

for i = 1:length(trials)
    for j = 1:5
        power(i,j) = anovaPowerExperiment(bins, lambda, lambda, 0, trials(i), alpha)

p = mean(power');
sdp = std(power');

semilogx(trials, p, 'k.')
set(gcf, 'Position', [360 669 308 265])
set(gca, 'NextPlot', 'add')

for i = 1:length(trials)
    semilogx([trials(i) trials(i)], [p(i)-sdp(i) p(i)+sdp(i)], 'k')

set(gca, 'YLim', [0 .1])
set(gca, 'XLim', [min(trials)/2 max(trials)*2])

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