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Hierarchical Bayesian inference in networks of spiking neurons

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If you have a question about this talk, please contact Philip Sterne .

This week we will be looking at: Rao (2005) – Hierarchical Bayesian inference in networks of spiking neurons Available from: http://www.cs.washington.edu/homes/rao/rao_nips04.pdf

Abstract:

There is growing evidence from psychophysical and neurophysiological studies that the brain utilizes Bayesian principles for inference and decision making. An important open question is how Bayesian inference for arbitrary graphical models can be implemented in networks of spiking neurons. In this paper, we show that recurrent networks of noisy integrate-and-fire neurons can perform approximate Bayesian inference for dynamic and hierarchical graphical models.

This talk is part of the Machine Learning Journal Club series.

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