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Probabilistic Inference in General Graphical Models with Stochastic Networks of Spiking Neurons

This paper by Pecevski, Buesing, and Maass (2011) explores a novel approach to probabilistic inference in graphical models through sampling techniques applied to networks of spiking neurons. The authors present a framework that leverages the properties of stochastic spiking networks to efficiently perform inference tasks, highlighting the advantages of implementing neural-like models for complex probability distributions. This work contributes to the understanding of computational mechanisms underlying inference in both biological and artificial neural networks.

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Probabilistic Inference in General Graphical Models with Stochastic Networks of Spiking Neurons

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  1. Neural Bayesian Inference Pecevski, Buesing, and Maass, 2011: Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons

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