Neural competition and probabilistic representations

Abstract

Perception and action show tight links to the statistical structure of physical stimuli and likely rewards, but the underlying mechanisms are unknown. A simple, biologically plausible network model shows that probabilistic behavior emerges naturally in diverse scenarios, and arises from sampling of competing responses. Notably, it also provides a principled computational rationale for the prevalent finding of balanced excitation and inhibition in the brain. Recurrent connections within a population of excitatory neurons embed multiple attractor states, and coupling to a pool of inhibitory neurons enforces mutual exclusivity among the states. Upon concurrent stimulation, competing attractors alternate in activity. These global state transitions are caused by local, uncorrelated spiking noise and yet convey, over time, the relative strengths of attractors' support. The simplest probabilistic competitive recurrent networks (PCRNs) allow for closed-form analysis, shedding light on the neural basis of choice behavior under uncertainty. More complex systems of laterally connected PCRNs can collectively resolve the myriad local ambiguities pervasive in sensory stimuli, rapidly settling into globally-consistent configurations that match perceptual reports. Alternations are crucial in all cases, and occur only if a PCRN's inhibitory pool is strong enough to prevent attractors' activity from reaching saturation. A balance between excitation and inhibition is thus both a prerequisite and a hallmark of probabilistic sampling in cortical networks.