Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources

09/27/2022
by   Bariscan Bozkurt, et al.
5

Extraction of latent sources of complex stimuli is critical for making sense of the world. While the brain solves this blind source separation (BSS) problem continuously, its algorithms remain unknown. Previous work on biologically-plausible BSS algorithms assumed that observed signals are linear mixtures of statistically independent or uncorrelated sources, limiting the domain of applicability of these algorithms. To overcome this limitation, we propose novel biologically-plausible neural networks for the blind separation of potentially dependent/correlated sources. Differing from previous work, we assume some general geometric, not statistical, conditions on the source vectors allowing separation of potentially dependent/correlated sources. Concretely, we assume that the source vectors are sufficiently scattered in their domains which can be described by certain polytopes. Then, we consider recovery of these sources by the Det-Max criterion, which maximizes the determinant of the output correlation matrix to enforce a similar spread for the source estimates. Starting from this normative principle, and using a weighted similarity matching approach that enables arbitrary linear transformations adaptable by local learning rules, we derive two-layer biologically-plausible neural network algorithms that can separate mixtures into sources coming from a variety of source domains. We demonstrate that our algorithms outperform other biologically-plausible BSS algorithms on correlated source separation problems.

READ FULL TEXT

page 9

page 27

page 28

page 30

research
06/01/2017

Blind nonnegative source separation using biological neural networks

Blind source separation, i.e. extraction of independent sources from a m...
research
10/09/2022

Correlative Information Maximization Based Biologically Plausible Neural Networks for Correlated Source Separation

The brain effortlessly extracts latent causes of stimuli, but how it doe...
research
06/28/2014

Convex Analysis of Mixtures for Separating Non-negative Well-grounded Sources

Blind Source Separation (BSS) has proven to be a powerful tool for the a...
research
04/11/2020

Blind Bounded Source Separation Using Neural Networks with Local Learning Rules

An important problem encountered by both natural and engineered signal p...
research
11/17/2021

A Normative and Biologically Plausible Algorithm for Independent Component Analysis

The brain effortlessly solves blind source separation (BSS) problems, bu...
research
05/02/2022

An Information Maximization Based Blind Source Separation Approach for Dependent and Independent Sources

We introduce a new information maximization (infomax) approach for the b...
research
01/16/2022

Modeling the Repetition-based Recovering of Acoustic and Visual Sources with Dendritic Neurons

In natural auditory environments, acoustic signals originate from the te...

Please sign up or login with your details

Forgot password? Click here to reset