Publications

Theoretical neuroscience and machine learning

Asterisks "*" indicate equal contribution

1. Shaping the distribution of neural responses with interneurons in a recurrent circuit model

   • David Lipshutz, Eero Simoncelli. Preprint. [arXiv, code]

2. Disentangling recurrent neural dynamics with stochastic representational geometry

   • David Lipshutz*, Amin Nejatbakhsh*, Alex Williams. ICLR 2024 Workshop on Representational Alignment (contributed talk). [openreview]

3. Neuronal temporal filters as normal mode extractors

   • Siavash Golkar*, Jules Berman*, David Lipshutz, Robert Haret, Tim Gollisch, Dmitri Chklovskii. Physical Review Research. [arXiv, DOI]

4. Adaptive whitening with fast gain modulation and slow synaptic plasticity

   • Lyndon Duong, Eero Simoncelli, Dmitri Chklovskii, David Lipshutz. NeurIPS 2023 (spotlight). [arXiv, openreview, proceedings, code]

5. Normative framework for deriving neural networks with multicompartmental neurons and non-Hebbian plasticity

   • David Lipshutz*, Yanis Bahroun*, Siavash Golkar*, Anirvan Sengupta, Dmitri Chklovskii. PRX Life. [arXiv, DOI]

6. Adaptive whitening in neural populations with gain-modulating interneurons

   • Lyndon Duong*, David Lipshutz*, David Heeger, Dmitri Chklovskii, Eero Simoncelli. ICML 2023. [arXiv, openreview, proceedings, code]

7. An online algorithm for contrastive principal component analysis

   • Siavash Golkar*, David Lipshutz*, Tiberiu Tesileanu, Dmitri Chklovskii. ICASSP 2023. [arXiv, proceedings, code]

8. Interneurons accelerate learning dynamics in recurrent neural networks for statistical adaptation

   • David Lipshutz, Cengiz Pehlevan, Dmitri Chklovskii. ICLR 2023. [arXiv, openreview, code]

9. A linear discriminant analysis model of imbalanced associative learning in the mushroom body compartment

   • David Lipshutz, Aneesh Kashalikar, Shiva Farashahi, Dmitri Chklovskii. PLoS Computational Biology. [bioRxiv, DOI, code]

10. Coordinated drift of receptive fields in Hebbian/anti-Hebbian network models during noisy representation learning

    • Shanshan Qin, Shiva Farashahi*, David Lipshutz*, Anirvan Sengupta, Dmitri Chklovskii, Cengiz Pehlevan. Nature Neuroscience. [bioRxiv, DOI, code]

11. Biological learning of irreducible representations of commuting transformations

    • Alex Genkin, David Lipshutz, Siavash Golkar, Tiberiu Tesileanu, Dmitri Chklovskii. NeurIPS 2022. [openreview, proceedings]

12. Biologically plausible single-layer networks for nonnegative independent component analysis

    • David Lipshutz, Cengiz Pehlevan, Dmitri Chklovskii. Biological Cybernetics. [arXiv, DOI, code]

13. A biologically plausible neural network for multi-channel canonical correlation analysis

    • David Lipshutz*, Yanis Bahroun*, Siavash Golkar*, Anirvan Sengupta, Dmitri Chklovskii. Neural Computation. [arXiv, DOI, code]

14. A simple normative network approximates local non-Hebbian learning in the cortex

    • Siavash Golkar, David Lipshutz, Yanis Bahroun, Anirvan Sengupta, Dmitri Chklovskii. NeurIPS 2020. [arXiv, proceedings, workshop, code]

15. A biologically plausible neural network for slow feature analysis

    • David Lipshutz*, Charlie Windolf*, Siavash Golkar, Dmitri Chklovskii. NeurIPS 2020. [arXiv, proceedings, code]

Mathematics

Authors are listed in alphabetical order

16. Stability of synchronous slowly oscillating periodic solutions for systems of delay differential equations with coupled nonlinearity

    • David Lipshutz, Robert Lipshutz. Journal of Dynamics and Differential Equations. [arXiv, DOI]

17. Customer-server population dynamics in heavy traffic

    • Rami Atar, Prasenjit Karmakar, David Lipshutz. Stochastic Systems. [arXiv, DOI]

18. Heavy traffic limits for join the shortest estimated queue policy using delayed information 

    • Rami Atar, David Lipshutz. Mathematics of Operations Research. [preprint, DOI]

19. Sensitivity analysis for the stationary distribution of reflected Brownian motion in a convex polyhedral cone

    • David Lipshutz, Kavita Ramanan. Mathematics of Operations Research. [arXiv, DOI]

20. A Monte Carlo method for estimating sensitivities of reflected diffusions in convex polyhedral domains 

    • David Lipshutz, Kavita Ramanan. Stochastic Systems. [arXiv, DOI]

21. Pathwise differentiability of reflected diffusions in convex polyhedral domains 

    • David Lipshutz, Kavita Ramanan. Annales de l'Institut Henri Poincaré, Probabilités et Statistiques. [arXiv, DOI]

22. Exit time asymptotics for small noise stochastic delay differential equations. 

    • David Lipshutz. Discrete and Continuous Dynamical Systems, Series A. [arXiv, DOI]

23. Large deviations for the empirical measure of a diffusion via weak convergence methods 

    • Paul Dupuis, David Lipshutz. Stochastic Processes and their Applications. [preprint, DOI]

24. On directional derivatives of Skorokhod maps in convex polyhedral domains 

    • David Lipshutz, Kavita Ramanan. Annals of Applied Probability. [arXiv, DOI]

25. Existence, uniqueness, and stability of slowly oscillating periodic solutions for delay differential equations with nonnegativity constraints 

    • David Lipshutz, Ruth Williams. SIAM Journal on Mathematical Analysis. [reprint, DOI]