Hippocampal spiking model for context dependent behavior (Raudies & Hasselmo 2014)

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Accession:194882
Our model simulates the effect of context dependent behavior using discrete inputs to drive spiking activity representing place and item followed sequentially by a discrete representation of the motor actions involving a response to an item (digging for food) or the movement to a different item (movement to a different pot for food). This simple network was able to consistently learn the context-dependent responses.
Reference:
1 . Raudies F, Hasselmo ME (2014) A model of hippocampal spiking responses to items during learning of a context-dependent task. Front Syst Neurosci 8:178 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism: Hippocampus;
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):
Implementer(s): Raudies, Florian [florian.raudies at gmail.com];
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CodePublished
screenshots
README.html
binariness.m
errorarea.m
Figure3AAndFigure4.m
Figure3BAndFigure5.m
firingRateToSI.m
gpl-3.0.txt *
index2label.m
lifModel.m
ManySlotBuffer.m
meanWoutNaN.m
NetworkSimulation100Runs.mat
rasterPlotToFiringRate.m
semWoutNaN.m
spikingNetworkContextLearning.m
StackContainer.m
stdpModel.m
TimeBuffer.m
                            
function D = semWoutNaN(D, dim)
% semWoutNaN
%   D   - N-dimensional data matrix.
%   dim - Dimension to calculate mean over.
%
% RETURN
%   D   - (N-1)-dimensional data matrix. In Matlab dimension dim is
%         retained and set to the value of 1.
%
% DESCRIPTION
%   Calculate the standard error of the mean (SEM) of D along dimension dim 
%   excluding NaN entries.

%   Florian Raudies, 09/07/2014, Boston University.


Dim         = ones(1,length(size(D)));
Dim(dim)    = size(D,dim);
Index       = isnan(D);
D(Index)    = 0;

% Number of elements in that dimension.
N  = size(D,dim) - sum(Index,dim);
% Mean over that dimension.
M = sum(D,dim)./(eps+N);
% Standard error of the mean.
D = sqrt(sum((~Index).*(D - repmat(M,Dim)).^2,dim))./(eps+N);