Connectionists: Probabilistic inference in general graphical models with noisy spiking neurons

[Pecevski Dejan]

Dear All,

I would like to draw your attention to a newly published paper in PLoS 
Computational Biology, titled

*Probabilistic Inference in General Graphical Models through Sampling in 
Stochastic Networks of Spiking Neurons*

by

Dejan Pecevski, Lars Buesing and Wolfgang Maass,

available online at

http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002294.

The paper builds on the results of a recent paper by Buesing et al., 
published in PLoS Computational Biology and
available online at 
http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.1002211.

Abstract:

An important open problem of computational neuroscience is the generic
organization of computations in networks of neurons in the brain. We show
here through rigorous theoretical analysis that inherent stochastic 
features
of spiking neurons, in combination with simple nonlinear computational
operations in specific network motifs and dendritic arbors, enable 
networks of
spiking neurons to carry out probabilistic inference through sampling in
general graphical models. In particular, it enables them to carry out
probabilistic inference in Bayesian networks with converging arrows
(``explaining away'') and with undirected loops, that occur in many 
real-world tasks.
Ubiquitous stochastic features of networks of spiking neurons, such as 
trial-to-trial
variability and spontaneous activity, are necessary ingredients of the 
underlying
computational organization. We demonstrate through computer simulations 
that
this approach can be scaled up to neural emulations of probabilistic 
inference
in fairly large graphical models, yielding some of the most complex 
computations
that have been carried out so far in networks of spiking neurons.

-- 
_______________________________________________
Dejan Pecevski, PhD
Institute for Theoretical Computer Science
Graz University of Technology
Inffeldgasse 16b, A-8010 Graz, Austria
Tel. +43 316 873 5849

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