Topic: trial outcome

A pivotal challenge in computational neuroscience is identifying sub-populations of neurons that encode behavior, perception, and memory, across varying contexts such as task difficulty or temporal progression. Therefore, analysis tools that provide model interpretability, beyond prediction performance, are essential for studying neuronal population activity. We propose a novel approach conditional-STochastic Gates (c-STG) that combines predictive modeling with contextual feature selection. c-STG comprises two networks: a hypernetwork that maps contextual variables to probabilistic feature sel