  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.
