Inferring the brain’s internal model from sensory responses in a probabilistic inference framework

During perception, the brain combines information received from its senses with prior information about the world (von Helmholtz, 1867) – a process whose neural basis is still unclear. If sensory neurons represent posterior beliefs in a Bayesian inference process, then they, just like the beliefs themselves, must depend both on sensory inputs and on prior information. We derive predictions for how prior knowledge relates a neuron’s stimulus tuning to its response covariability in a way specific to the psychophysical task performed by the brain, and for how this covariability arises from both feedforward and feedback signals. We show that our predictions are in agreement with existing measurements. Finally, we demonstrate how to use neurophysiological measurements to reverse-engineer information about the subject’s internal beliefs about the structure of the task. Our results reinterpret neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.