Christopher D. Harvey

This paper develops inverse reinforcement learning to infer evolving objectives from animal behavior and shows it can identify time-varying objectives in mice.

This paper introduces CURBD, a method using data-constrained recurrent neural networks to infer brain-wide interactions from neural recordings across species.

The paper uses data-derived agents to study neural dynamics in mouse cortex, revealing chaotic attractors that support robust and adaptable behavior.

The paper investigates whether cortical neurons projecting to the same target form specialized population codes, finding structured correlations that enhance information transmission during correct behavioral choices.

This study uses a calcium imaging-based brain-machine interface to show that posterior parietal cortex activity can directly drive navigational behavior in mice without requiring learning.

The paper investigates how synaptic connectivity in the posterior parietal cortex supports decision-making by identifying opponent inhibition motifs between excitatory and inhibitory neurons.

The paper identifies neurons in V1, PPC, and RSC that encode mixed selectivity for navigation decisions, forming efficient population codes in RSC for correct choices.

This study shows that spinal cord dorsal horn neurons integrate inputs from multiple mechanoreceptor subtypes, leading to diverse mechanical response profiles and influencing tactile representations in the brain.

The paper shows that mice with different cognitive experiences use distinct cortical areas and neural activity patterns for navigation decisions, highlighting past learning as a key factor in determining cortical necessity for decision-making.

DeepEthogram uses machine learning to automatically classify animal behaviors from video frames with high accuracy, reducing manual effort and increasing reproducibility.

The paper shows that neural correlations in association cortex enhance behavioral performance by improving information consistency, despite limiting sensory information.

The paper shows that mechanoreceptor synapses in the brainstem develop differently based on their proximity to glabrous skin, shaping the central representation of touch.

The paper investigates how information about visual stimuli is distributed across large neuronal populations in mouse V1, finding sublinear scaling due to correlated noise.

The paper shows that neural population drift in sensorimotor cortex is systematically constrained, allowing for stable task information despite apparent instability.

The paper uses a mouse V1 model to study how perturbations affect coding efficiency and robustness, showing shifts in inhibition and excitation based on stimulus contrast.

PESCA identifies cell-type-specific enhancers using ATAC- and single-cell RNA-sequencing to generate viral reagents for targeted gene manipulation in mammalian cells.

The paper shows that visual cortex activity influences perception, but this effect depends on task difficulty and network connectivity patterns.

The paper develops all-optical electrophysiology tools to record subthreshold dynamics and excitability in mouse hippocampus during behavior, revealing brain-state dependent changes in neuronal activity.