High-Dimensional Dynamics & Computation Group
David G. Clark
Group Leader & Associate Research Scientist (Starting September 2026)
Flatiron Institute, Center for Computational Neuroscience
Simons Foundation
Adjunct Assistant Professor of Neuroscience
Columbia University, Center for Theoretical Neuroscience
While I am highly enthusiastic about AI in the theoretical sciences, please refrain from using it in emails to me; I will do the same. Thank you!
I seek a conceptual understanding of how large neural circuits process information. To this end, I develop theories linking synaptic connectivity, neuronal dynamics, and computation. Concretely, I use tools from statistical physics and machine learning to analyze high-dimensional nonlinear network models and connect them to experimental data.
Key directions include how task demands sculpt network dynamics; the origins and dynamics of structured representations, such as manifolds and associative memories; novel forms of processing enabled by synaptic plasticity; and theory-driven interpretation of large-scale population recordings.
If you are interested in working with me as a postdoc or graduate student, please email me.
I am currently a Research Fellow at the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. I received a Ph.D. in Neurobiology and Behavior from Columbia University in 2025, where I was advised by Larry Abbott and worked with Ashok Litwin-Kumar, Haim Sompolinsky, and Richard Axel. Before that, I studied physics and computer science at UC Berkeley, where I did research with Kristofer Bouchard.
My publications are listed below, or see my Google Scholar. My CV is here.
Outside of science, I see a lot of Broadway.
Publications & preprints
- (2026). Transient dynamics of associative memory models. Physical Review E. JournalPDFarXiv
- (2026). Linear equivalence of nonlinear recurrent neural networks. arXiv. arXiv
- (2026). Structure, disorder, and dynamics in task-trained recurrent neural circuits. bioRxiv. bioRxivKempner blogCode
- (2025). A theory of multi-task computation and task selection. bioRxiv. bioRxiv
- (2025). Connectivity structure and dynamics of nonlinear recurrent neural networks. Physical Review X. JournalPDFarXiv
- (2025). Simplified derivations for high-dimensional convex learning problems. SciPost Physics Lecture Notes. JournalPDFarXiv
- (2025). Theories of structure, dynamics, and plasticity in neural circuits. Ph.D. thesis, Columbia University. Academic Commons
- (2025). Associative synaptic plasticity creates dynamic persistent activity. bioRxiv. bioRxiv
- (2025). Structure of activity in multiregion recurrent neural networks. PNAS. JournalPDFarXiv
- (2025). Symmetries and continuous attractors in disordered neural circuits. bioRxiv. bioRxiv
- (2024). Theory of coupled neuronal-synaptic dynamics. Physical Review X. JournalPDFarXivViewpointCode
- (2023). Dimension of activity in random neural networks. Physical Review Letters. JournalPDFarXiv
- (2021). Olfactory landmarks and path integration converge to form a cognitive spatial map. Neuron. JournalPDFVideoCode
- (2021). Credit assignment through broadcasting a global error vector. NeurIPS 2021. arXivCode
- (2019). Unsupervised discovery of temporal structure in noisy data with dynamical components analysis. NeurIPS 2019. arXivCode
- (2017). Neuromorphic Kalman filter implementation in IBM's TrueNorth. Journal of Physics: Conference Series. JournalPDF
Invited talks
- Oct 2026SIAM NY-NJ-PA Section Conference minisymposium on stochastic neuroscience, Rutgers University, New Brunswick
- Oct 2026Dynamics in Neuroscience Workshop, National Institute for Theory and Mathematics in Biology, Chicago
- Sep 2026Bernstein Conference workshop: “Quo vadis, neural network theory?” Frankfurt
- Jun 2026ICMNS mini-symposium: “Recent Advances in the Study of Disorder in Recurrent Neural Networks,” Montreal
- Mar 2026Dynamical Systems Seminar, Department of Mathematics and Statistics, Boston University
- Feb 2026Computational Neuroscience Seminar, Courant Institute, New York University
- Jan 2026van Vreeswijk Theoretical Neuroscience Seminar (virtual)
- Jan 2026Theoretical Physics for Artificial Intelligence, Aspen Center for Physics
- Nov 2025Simons Collaboration on the Physics of Learning and Neural Computation, kickoff workshop, Stanford University
- Jun 2025Gatsby Tri-Centre Meeting, University College London
- Jun 2025Shervin Safavi group, TU Dresden (virtual)
- Mar 2025CoSyNe workshop: “Collectively Emerged Timescales,” Montreal
- Sep 2024Cengiz Pehlevan group, Harvard University
- Sep 2024Xiao-Jing Wang group, New York University
- May 2024Youth in High Dimensions, International Centre for Theoretical Physics, Trieste
- May 2024Hakan Türeci group, Princeton University
- Feb 2024University of Washington Theoretical Neuroscience Journal Club (virtual)
- Dec 2023Rutgers 125th Statistical Mechanics Conference
- Sep 2023Bernstein Conference workshop: “Relationship Between Multi-level Network Connectivity and Neural Dynamics,” Berlin
- Jun 2023Junior Theoretical Neuroscientist Workshop, Flatiron Center for Computational Neuroscience
- Apr 2023Ilya Nemenman lab, Emory University
- Apr 2023Theoretical Neuroscience Journal Club organized by Xaq Pitkow (virtual)
- Feb 2023Wulfram Gerstner group, EPFL
- Feb 2023Les Houches workshop: “Toward a Theory of Artificial and Biological Neural Networks”
- Oct 2022Center for the Physics of Biological Function, Princeton University
- Sep 2022Redwood Center for Theoretical Neuroscience, UC Berkeley