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Statistical Analysis of Neural Data

We develop statistical models and open-source computational tools to extract insights from neural data.

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About

Our researchers are particularly interested in characterizing flexibility and variability in neural circuits — for example, how the dynamics of large neural ensembles change over the course of learning a new skill, during periods of high attention or task engagement, or during development and aging.

Summarizing these processes even on a descriptive level is a difficult and unsolved challenge in need of new analysis tools.

People

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Alex Williams

Associate Research Scientist, Center for Computational Neuroscience

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Philip Greengard

Associate Research Scientist, Center for Computational Neuroscience

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Andrew Mah

Flatiron Research Fellow, Center for Computational Neuroscience

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Sarah Jo Venditto

Associate Research Scientist, Center for Computational Neuroscience

Publications

How Data Augmentation Shapes Neural Representations

arXiv:2605.15306, May 14, 2026

Tianxiao He, A. Williams, and S. Harvey

Tracking the Fidelity of Internal Neural Representations with Error-In-Variables Regression

bioRxiv:2026.04.22.720005, May 12, 2026

Isabel Garon, Stephen Keeley, and A. Williams

Modulating Cross-Modal Convergence with Single-Stimulus, Intra-Modal Dispersion

arXiv:2604.21836, April 23, 2026

Eghbal A. Hosseini, Brian Cheung, Evelina Fedorenko, and A. Williams