Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019)

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Accession:260210
This model shows how spatial representations in 3D space could emerge using unsupervised neural networks. Model is a hierarchical one which means that it has multiple layers, where each layer has got a specific function to achieve. This architecture is more of a generalised one i.e. it gives rise to different kinds of spatial representations after training.
Reference:
1 . Soman K, Chakravarthy S, Yartsev MM (2018) A hierarchical anti-Hebbian network model for the formation of spatial cells in three-dimensional space. Nat Commun 9:4046 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Connectionist Network;
Brain Region(s)/Organism: Hippocampus; Entorhinal cortex;
Cell Type(s): Abstract rate-based neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: MATLAB;
Model Concept(s): Spatial Navigation; Learning; Unsupervised Learning;
Implementer(s): Soman, Karthik [karthi.soman at gmail.com];
function [v,lamda]=pca_own(X)
% X must be of the form dimension x #observation
Xmean=repmat(mean(X,2),1,size(X,2));
Xnor=X-Xmean;
cvr=Xnor*Xnor';
[v,lamda]=eig(cvr); 
end