Humans and other animals behave as if we perform fast Bayesian inference underlying decisions and movement control given uncertain sense data. Here we show that a biophysically realistic model of the subthreshold membrane potential of a single neuron can exactly compute the numerator in Bayes rule for inferring the Poisson parameter of a sensory spike train. A simple network of spiking neurons can construct and represent the Bayesian posterior density of a parameter of an external cause that affects the Poisson parameter, accurately and in real time.
@article{arxiv.1406.5115,
title = {Bayesian Inference with Spiking Neurons},
author = {Michael G. Paulin and Andre van Schaik},
journal= {arXiv preprint arXiv:1406.5115},
year = {2014}
}