John Patrick Cunningham

This paper introduces SCA, an unsupervised method for identifying interpretable latent factors in neural data, and shows its effectiveness across different datasets.

This study shows that motor units are flexibly controlled by the cortex, challenging the traditional view of common drive and rigid recruitment.

This paper introduces a cloud-based platform that provides automated, reproducible, and accessible data analysis tools for neuroscience, improving efficiency and reducing infrastructure barriers.

The paper shows that motor cortex activity during movement tasks reflects network-level strategies for generating muscle activity rather than direct muscle control.

This paper introduces a video analysis tool that combines supervised pose estimation with unsupervised dimensionality reduction to extract interpretable behavioral features from animal videos, improving downstream analyses.

This paper introduces CoDaCoRe, a deep learning method for identifying sparse log-ratio biomarkers from high-throughput sequencing data, achieving faster performance and better accuracy than existing methods.

This paper introduces emergent property inference (EPI), a method using deep learning to solve inverse problems in theoretical neuroscience by learning parameter distributions consistent with computational properties.

Each recorded population gets its own latent dynamics, a discrete state sets how the populations couple, and the switch rules say which population triggers the change.

This study identifies stable, separable representations of value and choice in OFC during decision-making using novel analysis methods.

The paper compares trajectory geometries in the supplementary motor area (SMA) and primary motor cortex (M1) to infer different computational roles, finding SMA trajectories are helical and M1 are elliptical.

This paper introduces LocaNMF, a method to decompose widefield calcium imaging data into interpretable spatial and temporal components for analyzing neural activity across mice.

The paper shows excitatory and inhibitory neurons in mice are equally selective during decision-making and develop similar selectivity changes during learning.

This paper introduces a clusterless decoding method using a switching Poisson hidden Markov model to analyze neural data without spike sorting, showing it can recover model parameters and uncover task-relevant structure from real data.

This study shows that preparatory neural events in monkeys are conserved across different movement initiation contexts, but preparation can be temporally flexible and brief.

The paper develops a Gaussian Process model to cancel electrical stimulation artifacts and detect neural spikes in multi-electrode recordings, achieving low error rates and real-time compatibility.

The paper develops a latent factor model to analyze shared variability in neural activity of zebra finch auditory cortex during song and synthetic sound stimulation, finding a simple structure dominated by one latent source.