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Adaptation, coding and Bayesian computations in single neurons and neural populations

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If you have a question about this talk, please contact Guillaume Hennequin.

Experimental evidence at the behavioral level shows that the brain is able to make Bayes-optimal decisions, yet at the circuit level little is known about how brains may implement Bayesian learning and inference. In this talk, we will investigate how spiking neurons can implement Bayesian computations both on the level of the single neuron and of a neural population. On the level of single neuron, we will use a Bayesian approach to derive a normative model of neural adaptation to the instantaneous input statistics. On the population level, we will investigate how neural variability can be exploited to implement inference by sampling and, in particular, Markov Chain Monte Carlo algorithms.

This talk is part of the Computational Neuroscience series.

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