Stability and computation in cortical circuits

Can cortical circuits self-organize into a stable asynchronous state despite massive amounts of recurrent excitation, without relying on single-neuronal saturation? I will show that strong and fast recurrent inhibition is sufficient to dynamically stabilize networks with strong recurrent excitation and an expansive rectified power-law nonlinearity.

I will then explore the consequences of such stabilization, and show how it accounts for various aspects of a wide range of contextual modulation effects like surround suppression and divisive normalization, which is a ubiquitous and canonical brain computation. Time allowing, I will also discuss some of the transient and time-dependent properties of such networks, in paricular how it can account for contextual influences on the characteristics of gamma rhythms in the visual cortex.