Joni D. Wallis

This study investigates how overt and covert attention influence decision-making dynamics in prefrontal cortex by analyzing neural activity in orbitofrontal cortex and lateral prefrontal cortex.

This paper develops a recurrent neural network model that learns to select computations by combining rational meta-reasoning with meta-learning, demonstrating its ability to approximate optimal symbolic models and replicate neural dynamics in macaque and human planning tasks.

This study identifies neural encoding of the decisional reference point in macaques, showing that only the ventral bank of the anterior cingulate cortex encodes it at the population level.

This study shows that the primate orbitofrontal cortex is specifically involved in value-based decisions but not perceptual decisions, indicating functional specialization.

This study uses Neuropixel recordings in rhesus macaques to show how frontostriatal circuits implement cognitive control through distinct neural dynamics during automatic and deliberative decisions.

The paper investigates how the primate hippocampus and orbitofrontal cortex interact to enable context-dependent decision-making in a state-dependent choice task.

This study shows distributional reinforcement learning better explains prefrontal cortex responses compared to classic reinforcement learning.

The paper compares neural representations in motor cortex, prefrontal cortex, and striatum during brain-machine interface and manual reaching tasks in nonhuman primates.

The paper analyzes neural population activity in the anterior cingulate cortex of monkeys to show how context is abstractly represented and how outcomes are encoded relative to this context.

The paper identifies two distinct neural coding components in the motor cortex during unimanual movements, showing how bilateral arm signals are separated and interact.

This study shows that primate hippocampal neurons encode abstract value spaces through value place fields, similar to how place cells encode physical space in rodents.

An open-source Python algorithm decomposes neural power spectra into a dynamic aperiodic 1/f background and Gaussian oscillatory peaks without requiring predefined canonical frequency bands.

This study investigates how value representations are maintained in prefrontal cortex during delayed reward tasks, finding mixed dynamics that combine stable and dynamic features.

The paper compares persistent and dynamic hypotheses of working memory coding in prefrontal cortex using a spatial WM task and finds that dynamic population-level activity in high time-constant neurons supports WM.