Blind Nonnegative Source Separation Using Biological Neural Networks

Author:

Pehlevan Cengiz1,Mohan Sreyas2,Chklovskii Dmitri B.3

Affiliation:

1. Center for Computational Biology, Flatiron Institute, New York, NY, 10010, U.S.A.

2. Center for Computational Biology, Flatiron Institute, New York, NY 10010, U.S.A., and IIT Madras, Chennai, India 600036

3. Center for Computational Biology, Flatiron Institute, New York, NY 10010, U.S.A., and NYU Medical School, New York, NY 10016, U.S.A.

Abstract

Blind source separation—the extraction of independent sources from a mixture—is an important problem for both artificial and natural signal processing. Here, we address a special case of this problem when sources (but not the mixing matrix) are known to be nonnegative—for example, due to the physical nature of the sources. We search for the solution to this problem that can be implemented using biologically plausible neural networks. Specifically, we consider the online setting where the data set is streamed to a neural network. The novelty of our approach is that we formulate blind nonnegative source separation as a similarity matching problem and derive neural networks from the similarity matching objective. Importantly, synaptic weights in our networks are updated according to biologically plausible local learning rules.

Publisher

MIT Press - Journals

Subject

Cognitive Neuroscience,Arts and Humanities (miscellaneous)

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