Topic: hidden representations

Feedforward sensory processing in the brain is generally construed as proceeding through a hierarchy of layers, each constructing increasingly abstract and invariant representations of sensory inputs. This interpretation is at odds with the observation that activity in sensory processing layers is heavily modulated by contextual signals, such as cross modal information or internal mental states. While it is tempting to assume that such modulations bias the feedforward processing pathway towards detection of relevant input features given a context, this induces a dependence on the contextual st

Recently, both experimental and theoretical work has expanded from studying neural systems with one task at a time to studying them while performing multiple tasks. An open question is understanding how a brain area, a population of inter-connected neurons, can carry on different computations depending on the context. Computations can either share the neural population code, share some activity modes, be entirely separated in activity space, or anything in between. To study this theoretically, it is possible to train RNNs on multiple tasks and study the emerging hidden representations. The cha