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Surya Ganguli

Surya Ganguli

Assistant Professor of Applied Physics and, by courtesy, of Neurobiology, of Electrical Engineering and of Computer Science

Academic Appointments

Assistant Professor, Applied Physics

Honors & Awards

Investigator Award in Mathematical Modeling of Living Systems, Simons Foundation (2016)
McKnight Scholar Award, McKnight Endowment Fund for Neuroscience (2015)
Scholar Award in Human Cognition, James S. McDonnell Foundation (2014)
Outstanding Paper Award, Neural Information Processing Systems Foundation (2014)
Sloan Research Fellowship, Alfred P. Sloan Foundation (2013)
Terman Award, Stanford University (2012)
Career Award at the Scientific Interface, Burroughs Wellcome Foundation (2009)
Swartz Fellow in Computational Neuroscience, Swartz Foundation (2004)

Professional Education

Ph.D., UC Berkeley, Theoretical Physics (2004)
M.A., UC Berkeley, Mathematics (2004)
M.Eng., MIT, Electrical Engineering and Computer Science (1998)
B.S., MIT, Mathematics (1998)
B.S., MIT, Physics (1998)
B.S., MIT, Electrical Engineering and Computer Science (1998)

Publications

Maheswaranathan, N., Kastner, D. B., Baccus, S. A., & Ganguli, S. (2018). Inferring hidden structure in multilayered neural circuits. PLoS Computational Biology, 14(8), e1006291.

Campbell, M. G., Ocko, S. A., Mallory, C. S., Low, I. I., Ganguli, S., & Giocomo, L. M. (2018). Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation. Nature Neuroscience.

Pennington, J., Schoenholz, S., & Ganguli, S. (2018). The emergence of spectral universality in deep networks. Presented at the Artificial Intelligence and Statistics (AISTATS).

Zenke, F., & Ganguli, S. (2018). SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks. Neural Computation, 1–28.

Kadmon, J., & Ganguli, S. (2018). Statistical mechanics of low-rank tensor decomposition. Presented at the Neural Information Processing Systems (NIPS).

Deny, S., Lindsey, J., Ganguli, S., & Ocko, S. (2018). The emergence of multiple retinal cell types through efficient coding of natural movies. Presented at the Neural Information Processing Systems (NIPS).

Nayebi, A., Bear, D., Kubulius, J., Kar, K., Ganguli, S., Di Carlo, J., … Yamins, D. (2018). Task-Driven Convolutional Recurrent Models of the Visual System. Presented at the Neural Information Processing Systems (NIPS).

Williams, A. H., Kim, T. H., Wang, F., Vyas, S., Ryu, S. I., Shenoy, K. V., … Ganguli, S. (2018). Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis. Neuron.

Abbott, L. F., Angelaki, D. E., Carandini, M., Churchland, A. K., Dan, Y., Dayan, P., … Zador, A. M. (2017). An International Laboratory for Systems and Computational Neuroscience. NEURON, 96(6), 1213–18.

Hardcastle, K., Ganguli, S., & Giocomo, L. M. (2017). Cell types for our sense of location: where we are and where we are going. NATURE NEUROSCIENCE, 20(11), 1474–82.

Hardcastle, K., Maheswaranathan, N., Ganguli, S., & Giocomo, L. M. (2017). A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex. NEURON, 94(2), 375-?

Zenke, F., Gerstner, W., & Ganguli, S. (2017). The temporal paradox of Hebbian learning and homeostatic plasticity. Current Opinion in Neurobiology, 43, 166–76.

Nguyen-Vu, T. B., Zhao, G. Q., Lahiri, S., Kimpo, R. R., Lee, H., Ganguli, S., … Raymond, J. L. (2017). A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity. ELife, 6.

Yang, T., Yang, C. F., Chizari, M. D., Maheswaranathan, N., Burke, K. J., Borius, M., … Shah, N. M. (2017). Social Control of Hypothalamus-Mediated Male Aggression. Neuron, 95(4), 955–70.e4.

Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., & Sohl-Dickstein, J. (2017). On the expressive power of deep neural networks. Presented at the International Conference on Machine Learning (ICML).

Ke, R., Goyal, A., Ganguli, S., & Bengio, Y. (2017). Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net. Presented at the Neural Information Processing Systems (NIPS).

Zenke, F., Poole, B., & Ganguli, S. (2017). Continual Learning with Intelligent Synapses. Presented at the International Conference on Machine Learning (ICML) .

Schoenholz, S., Gilmer, J., Ganguli, S., & Sohl-Dickstein, J. (2017). Deep information propagation. Presented at the International Conference on Learning Representations (ICLR) .

Pennington, J., Schoenholz, S., & Ganguli, S. (2017). Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice. Presented at the Neural Information Processing Systems (NIPS).

Advani, M., & Ganguli, S. (2016). Statistical Mechanics of Optimal Convex Inference in High Dimensions. PHYSICAL REVIEW X, 6(3).

Leong, J. C. S., Esch, J. J., Poole, B., Ganguli, S., & Clandinin, T. R. (2016). Direction Selectivity in Drosophila Emerges from Preferred-Direction Enhancement and Null-Direction Suppression. Journal of Neuroscience , 36(31), 8078–92.

Poole, B., Subhaneil, L., Raghu, M., Sohl-Dickstein, J., & Ganguli, S. (2016). Exponential expressivity in deep neural networks through transient chaos. Presented at the Neural Information Processing Systems (NIPS), Centre Convencions Internacional Barcelona, Barcelona Spain.

McIntosh, L. T., Maheswaranathan, N., Nayebi, A., Ganguli, S., & Baccus, S. A. (2016). Deep Learning Models of the Retinal Response to Natural Scenes. Advances in Neural Information Processing Systems, 29, 1369–77.

Advani, M., & Ganguli, S. (2016). An equivalence between high dimensional Bayes optimal inference and M-estimation. Presented at the Neural Information Processing Systems (NIPS).

Bouchard, K. E., Ganguli, S., & Brainard, M. S. (2015). Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences. FRONTIERS IN COMPUTATIONAL NEUROSCIENCE, 9.

Gao, P., & Ganguli, S. (2015). On simplicity and complexity in the brave new world of large-scale neuroscience. CURRENT OPINION IN NEUROBIOLOGY, 32, 148–155.

Hardcastle, K., Ganguli, S., & Giocomo, L. M. (2015). Environmental Boundaries as an Error Correction Mechanism for Grid Cells. NEURON, 86(3), 827–839.

Giret, N., Kornfeld, J., Ganguli, S., & Hahnloser, R. H. R. (2014). Evidence for a causal inverse model in an avian cortico-basal ganglia circuit. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 111(16), 6063–6068.

Dickstein, J. S., Poole, B., & Ganguli, S. (2014). Fast large scale optimization by unifying stochastic gradient and quasi-Newton methods. Presented at the International Conference on Machine Learning (ICML).

Saxe, A., McClelland, J., & Ganguli, S. (2014). Exact solutions to the nonlinear dynamics of learning in deep neural networks. Presented at the International Conference on Learning Representations (ICLR).

Dauphin, Y., Pascanu, R., Gulchere, C., Cho, K., Ganguli, S., & Bengio, Y. (2014). Identifying and attacking the saddle point problem in high-dimensional non-convex optimization. Presented at the Neural Information Processing Systems (NIPS).

Kao, J. C., Nuyujukian, P., Stavisky, S., Ryu, S. I., Ganguli, S., & Shenoy, K. V. (2013). Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness. Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, 2013, 293–298.

Hanuschkin, A., Ganguli, S., & Hahnloser, R. H. R. (2013). A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models. FRONTIERS IN NEURAL CIRCUITS, 7.

Advani, M., Lahiri, S., & Ganguli, S. (2013). Statistical mechanics of complex neural systems and high dimensional data. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT.

Lahiri, S., & Ganguli, S. (2013). A memory frontier for complex synapses. Presented at the Neural Information Processing Systems (NIPS).

Saxe, A., McClelland, J., & Ganguli, S. (2013). Learning hierarchical category structure in deep neural networks. Presented at the Proceedings of the Cognitive Science Society.

Hahnloser, R., & Ganguli, S. (2013). Vocal learning with inverse models. Principles of Neural Coding. CRC Press.

Kim, S. M., Ganguli, S., & Frank, L. M. (2012). Spatial Information Outflow from the Hippocampal Circuit: Distributed Spatial Coding and Phase Precession in the Subiculum. JOURNAL OF NEUROSCIENCE, 32(34), 11539–11558.

Ganguli, S., & Sompolinsky, H. (2012). Compressed Sensing, Sparsity, and Dimensionality in Neuronal Information Processing and Data Analysis. ANNUAL REVIEW OF NEUROSCIENCE, VOL 35, 35, 485–508.

Gangui, S., & Sompolinsky, H. (2010). Short-term memory in neuronal networks through dynamical compressed sensing. Presented at the Neural Information Processing Systems (NIPS).

Ganguli, S., & Latham, P. (2009). Feedforward to the Past: The Relation between Neuronal Connectivity, Amplification, and Short-Term Memory. NEURON, 61(4), 499–501.

Ganguli, S., Huh, D., & Sompolinsky, H. (2008). Memory traces in dynamical systems. PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 105(48), 18970–18975.

Ganguli, S., Bisley, J. W., Roitman, J. D., Shadlen, M. N., Goldberg, M. E., & Miller, K. D. (2008). One-dimensional dynamics of attention and decision making in LIP. NEURON, 58(1), 15–25.

Lau, K.-Y., Ganguli, S., & Tang, C. (2007). Function constrains network architecture and dynamics: A case study on the yeast cell cycle Boolean network. PHYSICAL REVIEW E, 75(5).

Brown, J., Ganguli, S., Ganor, O., & Helfgott, C. (2005). E10 Orbifolds. Journal of High Energy Physics, 06(057).

Ganguli, S., Ganor, O. J., & Gill, J. (2004). Twisted six dimensional gauge theories on tori, matrix models, and integrable systems. JOURNAL OF HIGH ENERGY PHYSICS, (9).

Boyda, E. K., Ganguli, S., Horava, P., & Varadarajan, U. (2003). Holographic protection of chronology in universes of the Godel type. PHYSICAL REVIEW D, 67(10).