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Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuitsyet its computational role still remains elusive. We show that a model ofGamma-band rhythmic inhibition allows networks of coupled cortical circuitmotifs to search for network configurations that best reconcile external inputswith an internal consistency model encoded in the network connectivity. We showthat Hebbian plasticity allows the networks to learn the consistency model byexample. The search dynamics driven by rhythmic inhibition enable the describednetworks to solve difficult constraint satisfaction problems without makingassumptions about the form of stochastic fluctuations in the network. We showthat the search dynamics are well approximated by a stochastic samplingprocess. We use the described networks to reproduce perceptual multi-stabilityphenomena with switching times that are a good match to experimental data andshow that they provide a general neural framework which can be used to modelother 'perceptual inference' phenomena.

Rhythmic inhibition allows neural networks to search for maximally consistent states