Topic: Flexible decision-making

The prefrontal cortex (PFC) is critical for flexible decision-making, and dynamical motifs have been identified in PFC population activity that could support certain aspects of this process (e.g., context-dependent line attractors for sensory input integration). PFC is also known to encode a plethora of decision-relevant variables, including task progression, action, and value. It remains an open question whether there are dynamical motifs that track all these variables to inform decisions. Further, it is unclear how outcome feedback alters neural representations of these variables and impacts

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

Abstraction and generalization are essential for flexible decision-making in novel situations. Recent work in humans and monkeys has shown how abstract variables are encoded by the representational geometry of single-neuron population activity in a format that supports generalization. However, these observations typically are made after learning has converged, leaving open the question of how these representations form. We designed the present study to tackle this question. We developed a factorized model of temporal abstraction that builds on the successor representation and is motivated by r