ModelDB: Deep belief network learns context dependent behavior (Raudies, Zilli, Hasselmo 2014)

Deep belief network learns context dependent behavior (Raudies, Zilli, Hasselmo 2014)

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Citations

Accession:194883

We tested a rule generalization capability with a Deep Belief Network (DBN), Multi-Layer Perceptron network, and the combination of a DBN with a linear perceptron (LP). Overall, the combination of the DBN and LP had the highest success rate for generalization.
Reference:
1 . Raudies F, Zilli EA, Hasselmo ME (2014) Deep belief networks learn context dependent behavior. PLoS One 9:e93250 [PubMed]

Model Information (Click on a link to find other models with that property)

Model Type:	Connectionist Network;
Brain Region(s)/Organism:
Cell Type(s):
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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DeepBeliefNetwork.m

DoubleContextLearner.m

DoubleContextLearnerDBN.m

DoubleContextLearnerDBNaLP.m

DoubleContextLearnerMLP.m

DoubleContextTask.m

LinearPerceptron.m

MultiLayerPerceptronNetwork.m

RestrictedBoltzmannMachine.m

rotateXLabels.m *

function S = num2cellstr(N)
% num2cellstr
%   N       - Integer numbers.
%
% RETURN
%   S       - Cell array with numbers as strings.
%
%
%   Florian Raudies, 01/30/2014, Boston University.

nNum = numel(N);
S    = cell(nNum,1);
for iNum = 1:nNum, S{iNum} = num2str(N(iNum)); end

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