Neural Signal Multiplexing via Compressed Sensing

TL;DR

This paper introduces a novel compressed sensing model for multiplexing chaotic neural signals, enabling efficient transmission and demultiplexing in noisy neural environments, inspired by biological neural systems.

Contribution

It proposes a new compressed sensing framework for chaotic neural signal multiplexing based on the Hindmarsh-Rose model, addressing interference and noise challenges.

Findings

Successful demultiplexing of neural signals amidst $10^4$ noisy signals

Demonstrates robustness of the compressed sensing approach in neural signal transmission

Provides a biologically inspired model for neural multiplexing

Abstract

Transport of neural signals in the brain is challenging, owing to neural interference and neural noise. There is experimental evidence of multiplexing of sensory information across population of neurons, particularly in the vertebrate visual and olfactory systems. Recently, it has been discovered that in lateral intraparietal cortex of the brain, decision signals are multiplexed with decision-irrelevant visual signals. Furthermore, it is well known that several cortical neurons exhibit chaotic spiking patterns. Multiplexing of chaotic neural signals and their successful demultiplexing in the neurons amidst interference and noise, is difficult to explain. In this work, a novel compressed sensing model for efficient multiplexing of chaotic neural signals constructed using the Hindmarsh-Rose spiking model is proposed. The signals are multiplexed from a pre-synaptic neuron to its neighbouring post-synaptic neuron, in the presence of $10^4$ interfering noisy neural signals and demultiplexed using compressed sensing techniques.