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

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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;
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Simulation Environment: MATLAB;
Model Concept(s):
Implementer(s): Raudies, Florian [florian.raudies at gmail.com];
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Matlab
screenshots
README.html
DeepBeliefNetwork.m
DoubleContextLearner.m
DoubleContextLearnerDBN.m
DoubleContextLearnerDBN.m'
DoubleContextLearnerDBNaLP.m
DoubleContextLearnerMLP.m
DoubleContextTask.m
Figure2.m
Figure3A.m
Figure3B.m
Figure3C.m
Figure3D.m
Figure3E.m
Figure3F.m
Figure3G.m
Figure3H.m
Figure4B.m
Figure4C.m
Figure4D.m
gpl-3.0.txt *
LinearPerceptron.m
logistic.m
MultiLayerPerceptronNetwork.m
num2cellstr.m
RestrictedBoltzmannMachine.m
rotateXLabels.m *
                            
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