Surya Ganguli. Quantum Physics meets Brain Science on Optical Platform PHI LAB Physics & Informatics Laboratories Our Mission Our mission is to build simple, efficient and practical solvers for real-world problems in our information-intensive society. He presently runs the Neural Dynamics and Computation Lab at Stanford, where he aims to reverse engineer how networks of neurons and synapses cooperate across multiple scales of space and time to facilitate sensory perception, motor control, memory, and other cognitive functions. Leonidas Guibas 54 publications . Cited by. The following articles are merged in Scholar. Yann N. Dauphin, Razvan Pascanu, Caglar Gulcehre, Kyunghyun Cho, Surya Ganguli, Yoshua Bengio Computer Science Research output : Contribution to journal › Conference article › peer-review Yuandong Tian 36 publications . Zenke F, Gerstner W, Ganguli S. (2017) The temporal paradox of Hebbian learning and homeostatic plasticity. Try again later. CBMM Memos; All Publications; Views & Reviews; Datasets and Code; CBMM on Github; CBMM Publication Acknowledgement; Diversity. Jack Lindsey, Samuel A. Ocko, Surya Ganguli, Stéphane Deny: A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs. Undergraduate Summer Research Internships in Neuroscience. Lecture Videos; Undergraduate Lecture Series 2015; Undergraduate Lecture Series 2014; Summer … Emergent Elasticity in the Neural Code for Space. claim Claim with Google Claim with Twitter Claim with GitHub Claim with LinkedIn × Hey Surya Ganguli! Samuel A. Ocko, Kiah Hardcastle, Lisa Giocomo, and Surya Ganguli. My CV and resume.. My Dynamics of minimum probability flow learning. The guest lecture by Surya Ganguli shows how insights from statistical mechanics applied to the analysis of high-dimensional data can contribute to our understanding of how functions such as object categorization emerge in multi-layer neural networks. Coherent Ising machines - Quantum optics and neural network perspectives. Surya Ganguli Associate Professor of Applied Physics and , by courtesy, of Neurobiology and of Electrical Engineering Curriculum Vitae available Online Bio ACADEMIC APPOINTMENTS ... PUBLICATIONS • … ask whether spike sorting is necessary for investigation at thelevelofneuralpopulations.Theyshow that combining multiple neurons on each electrode introduces only minor distortions in estimates of neural population state. While at Berkeley, he taught undergraduate courses on Quantum Mechanics, Special Relativity, Statistical Physics, Electromagnetism, and Analytical Mechanics. Rev. View Soorjya Ganguli’s profile on LinkedIn, the world's largest professional community. Neuroscience Physics Machine Learning. Articles Cited by Co-authors. In order to navigate, animals need to maintain a representation of both their own position as well as their environment. [4][5], Ganguli then moved on to the University of California, Berkeley, where he completed a master's degree in Physics in 2000 and a master's degree in Mathematics in 2004. Claim your profile and join one of the world's largest A.I. Theoretical Neuroscience. (2017) A Multiplexed, Heterogeneous, and Adaptive Code for … Their, This "Cited by" count includes citations to the following articles in Scholar. Title. PHI PARTNERS Our Partners We are a team of quantum physicists, electrical engineers, and neuroscientists with the shared vision of building a next-generation computing machine. How are these representations learned and encoded, and how do they interact with each other? Overview. The system can't perform the operation now. BA Richards, TP Lillicrap, P Beaudoin, Y Bengio, R Bogacz, ... J Sohl-Dickstein, EA Weiss, N Maheswaranathan, S Ganguli, International Conference on Machine Learning, 604-612, New articles related to this author's research, Sir Henry Dale Fellow, Department of Experimental Psychology, University of Oxford, Postdoctoral Researcher, Department of Applied Physics, Stanford University, Professor of computer science, University of Montreal, Mila, IVADO, CIFAR, Professor of Physics, Racah Institute of Physics, Edmond and Lily Safra Center for Brain Sciences, Research Scientist at Deepmind and PhD from MILA, Postdoctoral Scientist (Neuroscience), Harvard University, Assistant Professor of Computer Science and Psychology, Stanford University, Institute of Neuroinformatics, ETH Zurich and University of Zurich, Identifying and attacking the saddle point problem in high-dimensional non-convex optimization, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, Continual learning through synaptic intelligence, On the expressive power of deep neural networks, Exponential expressivity in deep neural networks through transient chaos, Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis, On simplicity and complexity in the brave new world of large-scale neuroscience, Environmental boundaries as an error correction mechanism for grid cells, Holographic protection of chronology in universes of the Gödel type, Superspike: Supervised learning in multilayer spiking neural networks, Deep learning models of the retinal response to natural scenes, Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice, One-dimensional dynamics of attention and decision making in LIP, A multiplexed, heterogeneous, and adaptive code for navigation in medial entorhinal cortex, A deep learning framework for neuroscience, Deep unsupervised learning using nonequilibrium thermodynamics, Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods. 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Cbmm on GitHub ; CBMM publication Acknowledgement ; Diversity Alexandre Lacoste, Simon Lacoste-Julien Realizing a trainable associative...