Error-Gated Hebbian Rule: A Local Learning Rule for Principal and Independent Component Analysis.

ABSTRACT: We developed a biologically plausible unsupervised learning algorithm, error-gated Hebbian rule (EGHR)-β, that performs principal component analysis (PCA) and independent component analysis (ICA) in a single-layer feedforward neural network. If parameter β = 1, it can extract the subspace that major principal components span similarly to Oja's subspace rule for PCA. If β = 0, it can separate independent sources similarly to Bell-Sejnowski's ICA rule but without requiring the same number of input and output neurons. Unlike these engineering rules, the EGHR-β can be easily implemented in a biological or neuromorphic circuit because it only uses local information available at each synapse. We analytically and numerically demonstrate the reliability of the EGHR-β in extracting and separating m

SUBMITTER: Isomura T 

PROVIDER: S-EPMC5789861 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature