Topic: noisy sensory evidence

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