Cortical-like dynamics in recurrent E-I networks optimized for fast probabilistic inference

LENGYEL, MÁTÉ; HENNEQUIN, GUILLAUME; ECHEVESTE, RODRIGO

Lisboa

Conferencia; Computational and Systems Neuroscience (Cosyne) Meeting 2018; 2019

COSYNE EXECUTIVE COMMITTEE

The dynamics of sensory cortices show a suite of basic, ubiquitous features that have so far escaped a common, principled theoretical account. These include strong, inhibition-dominated transients at stimulus onset, gamma oscillations, and noise variability ? all stimulus-dependent. We present a unifying model in which all these dynamical phenomena emerge as a consequence of the efficient implementation of thesame computational function: fast probabilistic inference. For this, we used a novel approach and trained a recurrent E/I neural circuit model of a V1 hypercolumn. The network was required to modulate not only themean (as conventional) but also the variability of its stationary response distributions in order to match the corresponding input-dependent posterior distributions inferred by an ideal observer. The optimized neural circuit featured a number of remarkable computational and biological properties. Computationally, after training on a reduced stimulus set, it exhibited strong forms of generalization by producing near-optimal response distributions to novel inputs which required qualitatively different responses. Furthermore, the network discovered non-equilibrium dynamics, a state-of-the-art machine learning strategy to speed up inferences. The circuit also exhibited realistic biological properties for which it was not trained directly. It achieved divisive normalization and displayed marked transients at stimulus onset, as well as strong gamma oscillations, both scaling with stimulus contrast. Crucially, these dynamical phenomena did not emerge in a control network trained to match mean responses only (without modulating variability). Further analyses of transients and oscillations in the optimized network revealed distinct functional roles for them in speeding up inferences and made predictions that we confirmed in novel analyses of awake monkey V1 recordings. Our results offer a principled theoretical account of the basic motifs of cortical dynamics and predict further properties of these motifs that can be tested in future experiments.