Next talk of BME's Szilárd Colloquium:

 

The understanding of brain computations requires methods that read out neural activity on different spatial and temporal scales. Following signal propagation and integration across a neuron and recording the concerted activity of hundreds of neurons pose distinct challenges, and the design of imaging systems has been mostly focused on tackling one of the two operations. We developed a high-resolution, acousto-optic two-photon microscope with continuous three-dimensional (3D) trajectory and random-access scanning modes that reaches near-cubic-millimeter scan range and can be adapted to imaging different spatial scales. We performed 3D calcium and voltage imaging of action potential backpropagation and dendritic spike forward propagation at sub-millisecond temporal resolution in behaving mice.

 

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.