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  • Building Biological Intelligence Investigating how the ingredients of biological computation come together to produce a system vastly more powerful than its constituent parts

    Computational Vision Developing novel computational methods to study how the brain analyzes and represents visual information.

    High Dimensional Dynamics and Computation Developing theories linking synaptic connectivity, neuronal dynamics and computation.

    Information Processing in Neural Networks Investigating the dynamics, statistical properties and principles of information processing in biological neural circuits and artificial neural networks.

    Neural Circuits and Algorithms Studying how the brain processes complex sensory data to inform AI networks and mental health treatments.

    NeuroRSE Building open source software for computational and systems neuroscience.

    Statistical Analysis of Neural Data Developing statistical models and computational tools to extract insights from neural data.

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Flatiron Institute

Computational Neuroscience

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    Building Biological Intelligence Investigating how the ingredients of biological computation come together to produce a system vastly more powerful than its constituent parts

    Computational Vision Developing novel computational methods to study how the brain analyzes and represents visual information.

    High Dimensional Dynamics and Computation Developing theories linking synaptic connectivity, neuronal dynamics and computation.

    Information Processing in Neural Networks Investigating the dynamics, statistical properties and principles of information processing in biological neural circuits and artificial neural networks.

    Neural Circuits and Algorithms Studying how the brain processes complex sensory data to inform AI networks and mental health treatments.

    NeuroRSE Building open source software for computational and systems neuroscience.

    Statistical Analysis of Neural Data Developing statistical models and computational tools to extract insights from neural data.

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Flatiron Institute Computational Neuroscience

Computational Neuroscience: Publications

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  1. The surprising secret identity of the semidefinite relaxation of K-means: manifold learning

    arXiv:1706.06028, June 19, 2017

    M. Tepper, A.M. Sengupta, and D. Chklovskii

  2. Resource-efficient perceptron has sparse synaptic weight distribution

    Signal Processing and Communications Applications Conference (SIU), 2017 25th, May 16, 2017

    C. Pehlevan and A. Sengupta

  3. The comprehensive connectome of a neural substrate for ‘ON’ motion detection in Drosophila

    eLife, 2017

    D. Chklovskii, S.Takemura, A. Nern, L. Scheffer, G. Rubin, and I. Meinertzhagen

  4. The comprehensive connectome of a neural substrate for ‘ON’ motion detection in Drosophila

    eLife, April 22, 2017

    S. Takemura, A. Nern, D. Chklovskii, L.K. Scheffer, G. Rubin, and I.A. Meinertzhagen

  5. Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?

    arXiv:1703.07914, March 23, 2017

    C. Pehlevan, A. Sengupta, and D. Chklovskii

  6. Cerebellar granule cells acquire a widespread predictive feedback signal during motor learning

    Nature Neuroscience, March 20, 2017

    A. Giovannucci, et al.

  7. Integrative neuromechanics of crawling in D. melanogaster larvae

    eLife, July 25, 2016

    C. Pehlevan, P. Paoletti, and L. Mahadevan

  8. Neocortex: a lean mean memory storage machine

    Nature, 2016

    D. Chklovskii, B. Mizusaki, A. Stepanyants, and P. Sjöström

  9. Neocortex: a lean mean storage machine

    Nature Neuroscience, April 26, 2016

    B.E.P Mizusaki, A. Stepanyants, D. Chklovskii, and P.J. Sjöström

  10. Derivation of Neural Circuits from the Similarity Matching Principle

    APS March Meeting 2016, 2016

    C. Pehlevan and D. Chklovskii

  11. Do retinal ganglion cells project natural scenes to their principal subspace and whiten them?

    2016 50th Asilomar Conference on Signals, Systems and Computers, March 6, 2016

    R. Abbasi-Asl, C. Pehlevan, B. Yu, and D. Chklovskii

  12. Self-calibrating neural networks for dimensionality reduction

    2016 50th Asilomar Conference on Signals, Systems and Computers, March 6, 2016

    Y. Chen, C. Pehlevan, and D. Chklovskii

  13. Acute off-target effects of neural circuit manipulations

    Nature, December 17, 2015

    T.M. Otchy, S.B.E. Wolff, J.Y. Rhee, C. Pehlevan, R. Kawai, A. Kempf, et al.

  14. A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks

    Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015

    C. Pehlevan and D. Chklovskii

  15. To Myelinate or Not to Myelinate?

    Neural Regeneration Research, December 1, 2015

    P. Monje

  16. Synaptic circuits and their variations within different columns in the visual system of Drosophila

    PNAS, November 3, 2015

    S. Takemura, C.S. Xu, Z. Lu, P.K. Rivlin, T. Parag, D.J. Olbris, et al.

  17. Synaptic circuits and their variations within different columns in the visual system of Drosophila

    PNAS, 2015

    D. Chklovskii, S. Takemura, C. Shan Xu, Z. Lu, P. K. Rivlina, T. Parag, et al.

  18. Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit

    PLOS, 2015

    D. Chklovskii and A. Bharioke

  19. Automatic adaptation to fast input changes in a time-invariant neural circuit

    PLOS Computational Biology, August 6, 2015

    A. Bharioke and D. Chklovskii

  20. FNTM: A Server for Predicting Functional Networks of Tissues in Mouse

    Nucleic Acids Res, 2015

    J. Goya, A. Wong, V. Yao, A. Krishnan, M. Homilius, and O. Troyanskaya

  21. A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data

    arXiv, March 2, 2015

    C. Pehlevan, T. Hu, and D. Chklovskii

  22. A Hebbian/Anti-Hebbian Network Derived from Online Non-Negative Matrix Factorization Can Cluster and Discover Sparse Features

    arXiv, March 2, 2015

    C. Pehlevan and D. Chklovskii

  23. A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans

    The Royal Society Publishing, 2015

    D. Chklovskii, V. J. Butler, R. Branicky, E.Yemini, J. F. Liewald, A. Gottschalk, et al.

  24. A Hebbian/Anti-Hebbian Neural Network for Linear Subspace Learning: A Derivation from Multidimensional Scaling of Streaming Data

    MIT Press Direct, 2015

    D. Chklovskii, C. Pehlevan, and T. Hu

  25. A consistent muscle activation strategy underlies crawling and swimming in Caenorhabditis elegans

    Journal of The Royal Society Interface, November 26, 2014

    V. Butler, R. Branicky, E. Yemini, and R. Schafer

  26. A Hebbian/Anti-Hebbian Network for Online Sparse Dictionary Learning Derived from Symmetric Matrix Factorization

    2014 48th Asilomar Conference on Signals, Systems and Computers, arXiv:1503.00690, 2014

    T. Hu, C. Pehlevan, and D. Chklovskii

  27. A Neuron as a Signal Processing Device

    arXiv, May 12, 2014

    T. Hu, Z. Towfic, C. Pehlevan, A. Genkin, and D. Chklovskii

  28. Toward large-scale connectome reconstructions

    Current Opinion in Neurobiology, March 7, 2014

    S.M. Plaza, L.K. Scheffer, and D. Chklovskii

  29. Wiring economy can account for cell body placement across species and brain areas

    Current Biology, February 3, 2014

    M. Rivera-Alba, H. Peng, G. de Polavieja, and D. Chklovskii

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