A thermodynamically consistent chemical spiking neuron capable of autonomous Hebbian learning

TL;DR

This paper introduces a thermodynamically consistent chemical neuron capable of autonomous Hebbian learning, scalable to multiple inputs, with potential applications in synthetic biology and efficient pattern recognition.

Contribution

It presents a novel chemical reaction network that functions as a spiking neuron with Hebbian learning, maintaining thermodynamic consistency and biological plausibility.

Findings

Successfully learns frequency biases in input patterns

Demonstrates learning of correlations between input channels

Discusses resource requirements for non-linear activation functions

Abstract

We propose a fully autonomous, thermodynamically consistent set of chemical reactions that implements a spiking neuron. This chemical neuron is able to learn input patterns in a Hebbian fashion. The system is scalable to arbitrarily many input channels. We demonstrate its performance in learning frequency biases in the input as well as correlations between different input channels. Efficient computation of time-correlations requires a highly non-linear activation function. The resource requirements of a non-linear activation function are discussed. In addition to the thermodynamically consistent model of the CN, we also propose a biologically plausible version that could be engineered in a synthetic biology context.