Topic: decision-making task

A common way to advance our understanding of brain processing is to decode behavior from recorded neural signals. In order to study the neural correlates of learning a task, we would like to decode behavior across the entire timespan of learning, which can take multiple recording sessions across many days. However, decoding across sessions is hindered due to a high amount of session-to-session variability in neural recordings. Here, we propose utilizing key time points in the trials with high behavioral correlations across sessions, as well as a novel bootstrapping method and region-based conv

A major question in computational neuroscience is what computational strategies the brain employs to represent the external world, the internal states, and its decisions. An open debate is whether neurons are organized in specialized sub-populations to encode behaviorally relevant variables ("categorical" selectivity) or whether information on several variables is distributed across the neural population ("heterogeneous mixed" selectivity). With evidence being published in support of both views, it is unclear under which conditions the brain uses one or the other encoding strategy. We will p