James M. Murray

The paper develops models of spinal locomotor networks showing coordinated locomotion arises from cell-type-specific connectivity and speed-selective interneuron recruitment.

The paper shows that the mouse olfactory bulb tracks breathing rhythms and self-location, providing contextual information for olfactory processing.

This study shows that exploratory behavior during training biases the movement strategies mice use to reach a spatial target.

This study identifies distinct roles of thalamic nuclei in the refinement of spatial reach movements in mice, showing that Pf nuclei control direction refinement while VAL nuclei influence speed and overshoot.

The paper shows that passive exposure to task-relevant stimuli improves learning speed in mice and is effectively modeled by neural networks using unsupervised learning during exposure.

This paper proposes neural and circuit-level mechanisms for motor learning, showing how cortical and subcortical pathways support fast and slow learning processes.

The paper identifies shared and unique neural subspaces in motor cortex for arm and brain-machine interface control, revealing movement intention as a key representation.

This paper introduces a biologically plausible learning rule for recurrent neural networks that uses local information and random feedback to train networks for time-dependent tasks.

The paper develops a model of the striatum that learns and executes variable-speed sequences through cortical tutoring, addressing challenges in temporal rescaling, sequence flexibility, and biologically plausible learning.