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arXiv · 1406.5115

Bayesian Inference with Spiking Neurons

Michael G. Paulin·Andre van Schaik
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Abstract

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.

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Michael G. Paulin, Andre van Schaik. 2014-06-19. Bayesian Inference with Spiking Neurons. https://arxiv.org/abs/1406.5115

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