David G. Clark

David G. Clark

dgclark@fas.harvard.edu

Research Fellow, Kempner Institute at Harvard University

Hello!

I am a theoretical neuroscientist and research fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University.

Neural circuits are characterized by their large scale, nonlinear dynamics, complex recurrent interactions, and connections that change across multiple timescales. I use tools from statistical physics and machine learning to understand how these features enable computation and learning.

I earned my Ph.D. in Neurobiology and Behavior from Columbia University in the Center for Theoretical Neuroscience, where I was primarily advised by Larry Abbott and worked closely with Ashok Litwin-Kumar and Haim Sompolinsky. Before that, I studied physics and computer science at UC Berkeley.

My publications are listed below, or see Google Scholar. My CV is here.

Outside of science, I see a lot of Broadway.

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Publications & preprints

(2025). Associative synaptic plasticity creates dynamic persistent activity. bioRš›˜iv.

bioRš›˜iv

(2025). Symmetries and continuous attractors in disordered neural circuits. bioRš›˜iv.

bioRš›˜iv

(2024). Simplified derivations for high-dimensional convex learning problems. arXiv.

arXiv

(2024). Connectivity structure and dynamics of nonlinear recurrent neural networks. arXiv.

arXiv

(2024). Theory of coupled neuronal-synaptic dynamics. Physical Review X.

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(2023). Dimension of activity in random neural networks. Physical Review Letters.

PDF Journal arXiv

(2021). Olfactory landmarks and path integration converge to form a cognitive spatial map. Neuron.

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(2021). Credit assignment through broadcasting a global error vector. NeurIPS 2021.

arXiv Code

(2019). Unsupervised discovery of temporal structure in noisy data with dynamical components analysis. NeurIPS 2019.

arXiv Code

(2017). Neuromorphic Kalman filter implementation in IBM's TrueNorth. Journal of Physics: Conference Series.

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