Log In Sign Up

A Normative and Biologically Plausible Algorithm for Independent Component Analysis

by   Yanis Bahroun, et al.

The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive. In signal processing, linear BSS problems are often solved by Independent Component Analysis (ICA). To serve as a model of a biological circuit, the ICA neural network (NN) must satisfy at least the following requirements: 1. The algorithm must operate in the online setting where data samples are streamed one at a time, and the NN computes the sources on the fly without storing any significant fraction of the data in memory. 2. The synaptic weight update is local, i.e., it depends only on the biophysical variables present in the vicinity of a synapse. Here, we propose a novel objective function for ICA from which we derive a biologically plausible NN, including both the neural architecture and the synaptic learning rules. Interestingly, our algorithm relies on modulating synaptic plasticity by the total activity of the output neurons. In the brain, this could be accomplished by neuromodulators, extracellular calcium, local field potential, or nitric oxide.


page 5

page 6

page 11

page 16

page 17

page 19

page 23

page 27


Bio-NICA: A biologically inspired single-layer network for Nonnegative Independent Component Analysis

Blind source separation, the problem of separating mixtures of unknown s...

A biologically plausible neural network for multi-channel Canonical Correlation Analysis

Cortical pyramidal neurons receive inputs from multiple distinct neural ...

Information Bottleneck-Based Hebbian Learning Rule Naturally Ties Working Memory and Synaptic Updates

Artificial neural networks have successfully tackled a large variety of ...

Using noise to probe recurrent neural network structure and prune synapses

Many networks in the brain are sparsely connected, and the brain elimina...