de la Rocha, J. | bioRxiv Science

bioRxiv2018

Response outcomes gate the impact of expectations on perceptual decisions

Perceptual decisions are not only determined by current sensory information but are also influenced by recent experience. Can the magnitude of expectation-based decision biases be flexibly modulated based on the outcome of previous decisions? We trained rats in several two-alternative forced choice auditory tasks, where the probability to repeat the previous stimulus category was varied in blocks of trials. All rats capitalized on the serial correlations of the stimulus sequence by consistently exploiting a transition bias: a tendency to repeat or alternate their previous response using an internal trial-by-trial estimate of the sequence repeating probability. Surprisingly, this expectation bias was null in trials immediately following an error. The internal estimate however was not reset and it became effective again causing a bias after the next correct response. This ability to rapidly activate and inactivate the bias was captured by a non-linear generative model of rat behavior, whereby a reward-driven modulatory signal gated the use of the latent estimate of the environment statistics on the current decision. These results demonstrate that rats use behavioral outcomes to flexibly modulate how expectations derived from the recent history influence their decisions.

neuroscience↗

bioRxiv2016

UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network

In the idling brain, neuronal circuits often exhibit transitions between periods of sustained firing (UP state) and quiescence (DOWN state). Although these dynamics occur across multiple areas and behavioral conditions, the underlying mechanisms remain unclear. Here we analyze spontaneous population activity from the somatosensory cortex of urethane-anesthetized rats. We find that UP and DOWN periods are variable (i.e. non-rhythmic) and that the population rate shows no significant decay during UP periods. We build a network model of excitatory (E) and inhibitory (I) neurons that exhibits a new bistability between a quiescent state and a balanced state of arbitrarily low rate. Fluctuating inputs trigger state transitions. Adaptation in E cells paradoxically causes a marginal decay of E-rate but a marked decay of I-rate, a signature of balanced bistability that we validate experimentally. Our findings provide evidence of a bistable balanced network that exhibits non-rhythmic state transitions when the brain rests.

neuroscience↗