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

Probabilistic inference in discrete spaces can be implemented into networks of LIF neurons