A thermodynamically consistent chemical spiking neuron capable of autonomous Hebbian learning
This work addresses the challenge of implementing autonomous learning in synthetic biology or chemical systems, though it appears incremental as it builds on existing concepts of chemical neurons and Hebbian learning.
The authors tackled the problem of creating a fully autonomous chemical spiking neuron that learns input patterns via Hebbian learning, resulting in a scalable system demonstrated to learn frequency biases and correlations between input channels.
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.