  The means by which cortical neural networks are able to efficiently solve
inference problems remains an open question in computational neuroscience.
Recently, abstract models of Bayesian computation in neural circuits have been
proposed, but they lack a mechanistic interpretation at the single-cell level.
In this article, we describe a complete theoretical framework for building
networks of leaky integrate-and-fire neurons that can sample from arbitrary
probability distributions over binary random variables. We test our framework
for a model inference task based on a psychophysical phenomenon (the
Knill-Kersten optical illusion) and further assess its performance when applied
to randomly generated distributions. As the local computations performed by the
network strongly depend on the interaction between neurons, we compare several
types of couplings mediated by either single synapses or interneuron chains.
Due to its robustness to substrate imperfections such as parameter noise and
background noise correlations, our model is particularly interesting for
implementation on novel, neuro-inspired computing architectures, which can
thereby serve as a fast, low-power substrate for solving real-world inference
problems.
