Ian Kinsella

maskNMF improves demixing of dense calcium imaging data by denoising, sparsening, and using constrained non-negative matrix factorization to accurately recover neural activity.

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

This paper introduces a method to accurately estimate spikes and subthreshold waveforms from 1-photon voltage imaging in vivo, even with noise.

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 introduces EASE, a method that matches calcium imaging signals to EM-derived cell segments in mouse visual cortex, enabling dendritic functional analysis and denoising.

YASS sorts spikes from multi-electrode arrays with > 500 electrodes using neural network detection and denoising, parallelized nonparametric Bayesian clustering, and matching-pursuit deconvolution, outperforming manual sorting on primate retina recordings.

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.

~2-4x SNR gains and 20-300x compression on calcium and voltage imaging, from a patch-wise penalized matrix decomposition that splits signal from temporally uncorrelated noise without hyperparameter tuning.