Gamma-band rhythmic inhibition is a ubiquitous phenomenon in neural circuits
yet its computational role still remains elusive. We show that a model of
Gamma-band rhythmic inhibition allows networks of coupled cortical circuit
motifs to search for network configurations that best reconcile external inputs
with an internal consistency model encoded in the network connectivity. We show
that Hebbian plasticity allows the networks to learn the consistency model by
example. The search dynamics driven by rhythmic inhibition enable the described
networks to solve difficult constraint satisfaction problems without making
assumptions about the form of stochastic fluctuations in the network. We show
that the search dynamics are well approximated by a stochastic sampling
process. We use the described networks to reproduce perceptual multi-stability
phenomena with switching times that are a good match to experimental data and
show that they provide a general neural framework which can be used to model
other 'perceptual inference' phenomena
