Topic: self-motion cues

Animals often make use of information from multiple sensory systems, such as vision, proprioception, olfaction, and audition to guide their behavior. They must thus integrate these different sensory inputs or select one of them in order to effectively make decisions in the environment. How exactly the brain accomplishes this sensory integration and to what extent each signal contributes under what circumstances remains to be fully understood. We use a deep reinforcement learning model to study sensory integration in a navigation task in two different scenarios. In the first scenario, there is

Efficient behavior in our noisy and ambiguous world calls for the strategic use of the arising uncertainty by probabilistic inference [1]. Probabilistic inference in complex dynamic environments is challenging as it requires tracking not only individual latent variables but also the interactions among them. As a result, a neural circuit that optimally encodes these variables would need to be recurrently wired to relay information between them via feedback connections [2]. Given the additional cost of evolving and maintaining these feedback connections, we sought the circumstances under which t