Topic: decision variables

In reversal learning tasks, large-scale circuits in multiple brain areas are involved in encoding multiple decision variables, such as trial outcomes, action values and action switching. It is unknown how spatiotemporal patterns of cortical encoding differentially encode these variables and whether the encoding depends on the level of engagement in the task. Here, we used a combination of large-scale imaging techniques and computational methods to investigate the representation of reward-related decision variables at the widefield and single-neuron levels. Widefield imaging and unsupervised an

A prevailing theory in goal-directed decision-making is that the brain accumulates evidence over time to make perceptual decisions. However, even after two decades of studies on evidence accumulation, it is still unclear how neuronal populations cooperate to dynamically represent decision variables. This is because of a lack of tools for gaining experimental control over variables of interest to test their relationship to behavior. To address this gap, we develop a state-space control framework to causally interact with decision-related latent variables. To expose decision variables to target

Flexible decision making is vital for survival. Animals must continuously evaluate their environment, making choices to maximize future gains. To facilitate decision making, multiple brain regions represent values of choice options and update them based on environmental interactions, connecting past experiences with future plans. Yet, the neural mechanisms generating these representations remain unclear. Here, we hypothesize that the claustrum, a subcortical nucleus highly interconnected with the neocortex, encodes decision variables and aids in sustaining their representations in cerebral cor

In surveying the rich reservoir of neuronal computations and brain functions, a natural question arises: what unified characteristics influence the diverse and heterogeneous dynamical states and computations in highly recurrent cortical networks? Seminal studies suggest that large-scale information processing results from interactions between the task environment, neuronal biophysics, and cortical architecture. While experimental advances have provided detailed cortical connectivity data, i.e. the connectome, it remains unclear which are fundamental for cognitive processing. Theoretically, Rec