Blind nonnegative source separation using biological neural networks
arXiv:1706.00382 · doi:10.1162/neco_a_01007
Abstract
Blind source separation, i.e. 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 dataset 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.
Accepted for publication in Neural Computation
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- Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons
- A Normative and Biologically Plausible Algorithm for Independent Component Analysis