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

 Download zip file 
Help downloading and running models
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]
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];
/
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 [ha hl] = errorarea(X,MeanValue,StdValue,colorArea,colorLine)
% errorarea
%   X               - Horizontal axis.
%   MeanValue       - Values of mean.
%   StdValue        - Values of standard deviation.
%   colorArea       - Color for the area, which covers +- 1 STD.
%   colorLine       - Color for the line line, which shows the mean.
%
% RETURN
%   ha              - Handle to area.
%   hl              - Handle to line.
%

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

if nargin<4, colorArea = 'b'; end
if nargin<5, colorLine = 'k'; end

Xd = [X(:); flipud(X(:))];
Yd = [MeanValue(:)-StdValue(:); flipud(MeanValue(:)+StdValue(:))];
ha = fill(Xd,Yd,colorArea,'LineStyle','none'); hold on;
hl = plot(X(:),MeanValue(:),'-','LineWidth',2.0,'Color',colorLine); hold off;

Loading data, please wait...