A Normative and Biologically Plausible Algorithm for Independent Component Analysis
arXiv:2111.08858
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.
References in corpus (6)
- Drawing Inspiration from Biological Dendrites to Empower Artificial Neural Networks
- Learning Independent Features with Adversarial Nets for Non-linear ICA
- A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features
- Blind nonnegative source separation using biological neural networks
- Blind Bounded Source Separation Using Neural Networks with Local Learning Rules
- A Neural Network with Local Learning Rules for Minor Subspace Analysis