Connectionists: New paper: "Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons"

[Lars Buesing]

Dear colleagues,

I would like to draw your attention to a paper we recently published in
PLoS Computational Biology. The paper is available at:
http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002211



TITLE: 

Neural Dynamics as Sampling: A Model for Stochastic Computation in
Recurrent Networks of Spiking Neurons


AUTHORS:

Buesing, L., Bill, J., Nessler, B., Maass, W. 


ABSTRACT:

The organization of computations in networks of spiking neurons in the
brain is still largely unknown, in particular in view of the inherently
stochastic features of their firing activity and the experimentally
observed trial-to-trial variability of neural systems in the brain. In
principle there exists a powerful computational framework for stochastic
computations, probabilistic inference by sampling, which can explain a
large number of macroscopic experimental data in neuroscience and
cognitive science. But it has turned out to be surprisingly difficult to
create a link between these abstract models for stochastic computations
and more detailed models of the dynamics of networks of spiking neurons.
Here we create such a link, and show that under some conditions the
stochastic firing activity of networks of spiking neurons can be
interpreted as probabilistic inference via Markov chain Monte Carlo
(MCMC) sampling. Since common methods for MCMC sampling in distributed
systems, such as Gibbs sampling, are inconsistent with the dynamics of
spiking neurons, we introduce a different approach based on
non-reversible Markov chains, that is able to reflect inherent temporal
processes of spiking neuronal activity through a suitable choice of
random variables. We propose a neural network model and show by a
rigorous theoretical analysis that its neural activity implements MCMC
sampling of a given distribution, both for the case of discrete and
continuous time. This provides a step towards closing the gap between
abstract functional models of cortical computation and more detailed
models of networks of spiking neurons.


KEYWORDS:

Neural dynamics, sampling, MCMC, spiking neuron models




Kind regards,

Lars Busing


-------------

Lars Busing
The Gatsby Computational Neuroscience Unit
lars at gatsby.ucl.ac.uk