  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.

Neural and Evolutionary Computing;Artificial Intelligence;Neurons and Cognition