Mark D. Humphries

The paper shows mice transition from choice-driven to cue-driven strategies during learning, with OFC signaling influencing cortical representations of reward and error strategies.

This study shows stochastic choice explains variability in patch foraging decisions in humans and rats, making leaving variability independent of environmental rewards and using suboptimal internal reward rate estimates.

This study shows that primate motor cortex uses both rhythmic and discrete movement control strategies in parallel, with M1 converging to a limit-cycle and SMA diverging before reaching a helical spiral.

The paper identifies a computational bottleneck in basal ganglia output due to structural and dynamic limitations and proposes a solution involving basis functions to explain output neuron activity and control mechanisms.

The paper shows that motor cortex activity encodes spatial and temporal arm movement parameters independently through low-dimensional neural trajectories.

This paper introduces a probabilistic method to track choice strategies in decision-making tasks at trial resolution, showing it identifies learning and exploratory strategies across species and synthetic agents.

The paper shows that medial prefrontal cortex activity in rats represents different states of the world in distinct subspaces, allowing separate decoding and preventing interference.

The paper uses a Bayesian approach to map neuron connectivity in the mouse striatum, revealing asymmetries in connection probabilities and distances between neuron types.

This study evaluates how different calcium deconvolution algorithms affect inferred neural activity, correlations, and coding in calcium imaging data.

This study investigates abnormal reward-learning in cervical dystonia using computational modeling and finds that decreased D2 cortico-striatal synaptic plasticity explains impaired reversal learning.

The paper shows that reinforcement tagging helps maintain relevant environmental features in prefrontal cortex short-term memory during trial-and-error learning in rats.

This paper reviews computational models of Parkinson’s disease to understand its mechanisms, neural dynamics, and treatments.

The paper introduces a probabilistic algorithm that simulates decision-making in monkeys, matching neural dynamics and behavior, and maps to brain circuits.

The paper applies network science to analyze neural population activity, enabling visualization, quantification, and tracking of neural interactions and dynamics.

The paper shows that a spiral attractor network drives rhythmic locomotion in Aplysia, with consistent low-dimensional dynamics across preparations.