Topic: dynamic environments

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