Topic: contextual feature selection

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

Behavioural tasks requiring context-dependent selection and accumulation of noisy sensory evidence are a powerful tool to study flexible decision-making (Mante et al., 2013), and recent work revealed a surprisingly degree of variability in the underlying neural mechanisms across individual animals (Pagan et al., 2022). Here we study how this individual variability develops during learning in rats and recurrent neural networks (RNNs). First, we show that the number of hidden units in RNNs plays a crucial role in the resulting amount of variability across networks initialized with different rand