Competition of cortical clusters during on-demand visual learning in immersive virtual reality

Neural circuits support rapid visual learning. However, due to technical roadblocks, it is not known how visual circuits represent multiple features or how behaviorally relevant representations are selected for long-term memory. Here we developed Moculus, a head-mounted virtual reality platform for mice that covers the entire visual field, and allows binocular depth perception and full immersion. This controllable environment, combined with fast acousto-optical imaging, affords rapid visual learning and the uncovering of novel circuit substrates: both the control and reinforcement-associated visual cue coding neuronal assemblies are extended transiently to a near-saturating level. They formed partially orthogonal and overlapping clusters centered around hub cells with higher and earlier ramp-like responses, as well as locally increased connectivity. This temporally maximizes computational capability and allows competition between assemblies that encode behaviorally relevant information by stochastic fluctuation from trial-to-trial. The coding competition is driven by reinforcement feedback at the level of individual neurons.