Probabilistic Inference in General Graphical Models through Sampling in
Stochastic Networks of Spiking Neurons
D. Pecevski, L. Büsing, and W. Maass
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.
Reference: D. Pecevski, L. Büsing, and W. Maass.
Probabilistic inference in general graphical models through sampling in
stochastic networks of spiking neurons.
PLoS Computational Biology, 7(12):e1002294, 2011.