# Blind nonnegative source separation using biological neural networks

## TL;DR

This paper introduces a biologically plausible neural network approach for blind nonnegative source separation, formulating it as a similarity matching problem with local learning rules, suitable for online streaming data.

## Contribution

It presents a novel formulation of blind nonnegative source separation as a similarity matching problem with biologically plausible neural networks and local learning rules.

## Key findings

- Neural networks derived from the similarity matching objective perform blind nonnegative source separation.
- The approach is suitable for online streaming data scenarios.
- Synaptic weight updates follow biologically plausible local learning rules.

## 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.