A Normative and Biologically Plausible Algorithm for Independent Component Analysis

TL;DR

This paper introduces a biologically plausible online ICA algorithm that uses local synaptic updates modulated by output neuron activity, aligning with neural mechanisms.

Contribution

It presents a novel objective function and neural network model for ICA that is both online and biologically plausible, incorporating local learning rules.

Findings

The algorithm operates in real-time without large data storage.

Synaptic updates depend only on local biophysical variables.

Output activity modulates synaptic plasticity, mimicking biological neuromodulation.

Abstract

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.