Surya Ganguli
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Surya Gangulibiology Degrees
Biology
#7567
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#10509
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Neuroscience
#988
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#1029
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Physics Biology
Surya Ganguli's Degrees
- PhD Physics Stanford University
Why Is Surya Ganguli Influential?
(Suggest an Edit or Addition)According to Wikipedia, Surya Ganguli is a university professor at Stanford University and a visiting research professor at Google. Ganguli is primarily known for his work on neural networks and deep learning, although he has also published papers on theoretical physics. 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. He is also known for being a prolific public speaker and lecturer, having been invited to give over 200 talks at various universities, institutes, workshops, conferences, and symposiums since 2005.
Surya Ganguli's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Continual Learning Through Synaptic Intelligence (2017) (1495)
- Exact solutions to the nonlinear dynamics of learning in deep linear neural networks (2013) (1476)
- Deep Unsupervised Learning using Nonequilibrium Thermodynamics (2015) (1213)
- Identifying and attacking the saddle point problem in high-dimensional non-convex optimization (2014) (1199)
- Deep Knowledge Tracing (2015) (675)
- On the Expressive Power of Deep Neural Networks (2016) (601)
- A deep learning framework for neuroscience (2019) (509)
- Exponential expressivity in deep neural networks through transient chaos (2016) (478)
- SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks (2017) (361)
- Cortical layer–specific critical dynamics triggering perception (2019) (313)
- On simplicity and complexity in the brave new world of large-scale neuroscience (2015) (311)
- Pruning neural networks without any data by iteratively conserving synaptic flow (2020) (292)
- Memory traces in dynamical systems (2008) (286)
- Deep Information Propagation (2016) (266)
- Compressed sensing, sparsity, and dimensionality in neuronal information processing and data analysis. (2012) (228)
- Resurrecting the sigmoid in deep learning through dynamical isometry: theory and practice (2017) (206)
- A theory of multineuronal dimensionality, dynamics and measurement (2017) (204)
- Deep Learning Models of the Retinal Response to Natural Scenes (2017) (195)
- Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis (2017) (185)
- Environmental Boundaries as an Error Correction Mechanism for Grid Cells (2015) (184)
- A Multiplexed, Heterogeneous, and Adaptive Code for Navigation in Medial Entorhinal Cortex (2017) (179)
- Understanding self-supervised Learning Dynamics without Contrastive Pairs (2021) (162)
- Accurate Estimation of Neural Population Dynamics without Spike Sorting (2017) (160)
- A mathematical theory of semantic development in deep neural networks (2018) (149)
- Holographic Protection of Chronology in Universes of the Godel Type (2002) (147)
- The Emergence of Spectral Universality in Deep Networks (2018) (138)
- Statistical Mechanics of Deep Learning (2020) (136)
- One-Dimensional Dynamics of Attention and Decision Making in LIP (2008) (133)
- The temporal paradox of Hebbian learning and homeostatic plasticity (2017) (123)
- Task-Driven Convolutional Recurrent Models of the Visual System (2018) (121)
- Shared Cortex-Cerebellum Dynamics in the Execution and Learning of a Motor Task (2019) (113)
- Fast large-scale optimization by unifying stochastic gradient and quasi-Newton methods (2013) (111)
- Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation (2018) (109)
- Biologically inspired protection of deep networks from adversarial attacks (2017) (108)
- Fundamental bounds on the fidelity of sensory cortical coding (2020) (105)
- On the saddle point problem for non-convex optimization (2014) (97)
- Social Control of Hypothalamus-Mediated Male Aggression (2017) (95)
- An analytic theory of generalization dynamics and transfer learning in deep linear networks (2018) (93)
- Analyzing noise in autoencoders and deep networks (2014) (91)
- Universality and individuality in neural dynamics across large populations of recurrent networks (2019) (90)
- Deep Learning on a Data Diet: Finding Important Examples Early in Training (2021) (87)
- Spatial Information Outflow from the Hippocampal Circuit: Distributed Spatial Coding and Phase Precession in the Subiculum (2012) (83)
- Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel (2020) (82)
- Holistic Evaluation of Language Models (2022) (79)
- Statistical mechanics of compressed sensing. (2010) (69)
- Function constrains network architecture and dynamics: a case study on the yeast cell cycle Boolean network. (2006) (67)
- Embodied intelligence via learning and evolution (2021) (65)
- Direction Selectivity in Drosophila Emerges from Preferred-Direction Enhancement and Null-Direction Suppression (2016) (64)
- Statistical mechanics of complex neural systems and high dimensional data (2013) (63)
- A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs (2019) (56)
- Emergent elasticity in the neural code for space (2018) (56)
- Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics (2019) (55)
- Understanding Self-supervised Learning with Dual Deep Networks (2020) (55)
- Inferring hidden structure in multilayered neural circuits (2017) (53)
- A memory frontier for complex synapses (2013) (52)
- A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models (2013) (51)
- Beyond neural scaling laws: beating power law scaling via data pruning (2022) (51)
- Variational Walkback: Learning a Transition Operator as a Stochastic Recurrent Net (2017) (50)
- Statistical Mechanics of Optimal Convex Inference in High Dimensions (2016) (49)
- Short-term memory in neuronal networks through dynamical compressed sensing (2010) (49)
- An International Laboratory for Systems and Computational Neuroscience (2017) (49)
- Discovering Precise Temporal Patterns in Large-Scale Neural Recordings through Robust and Interpretable Time Warping (2019) (48)
- A unified theory for the origin of grid cells through the lens of pattern formation (2019) (48)
- Learning hierarchical category structure in deep neural networks (2013) (45)
- Evidence for a causal inverse model in an avian cortico-basal ganglia circuit (2014) (43)
- Neural Mechanics: Symmetry and Broken Conservation Laws in Deep Learning Dynamics (2020) (40)
- Improved multitask learning through synaptic intelligence (2017) (39)
- Cell types for our sense of location: where we are and where we are going (2017) (37)
- From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction (2019) (34)
- Coupling of activity, metabolism and behaviour across the Drosophila brain (2021) (34)
- A universal tradeoff between power, precision and speed in physical communication (2016) (30)
- The emergence of multiple retinal cell types through efficient coding of natural movies (2018) (30)
- A unified theory for the computational and mechanistic origins of grid cells (2020) (29)
- Emergent properties of the local geometry of neural loss landscapes (2019) (29)
- Vocal learning with inverse models (2013) (26)
- Deep learning models reveal internal structure and diverse computations in the retina under natural scenes (2018) (25)
- Coherent Ising machines—Quantum optics and neural network Perspectives (2020) (24)
- Two Routes to Scalable Credit Assignment without Weight Symmetry (2020) (24)
- Identification of cellular-activity dynamics across large tissue volumes in the mammalian brain (2017) (24)
- Distance-tuned neurons drive specialized path integration calculations in medial entorhinal cortex (2021) (23)
- RNNs Can Generate Bounded Hierarchical Languages with Optimal Memory (2020) (22)
- Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution (2022) (22)
- A neural circuit state change underlying skilled movements (2021) (20)
- Enhancing Associative Memory Recall and Storage Capacity Using Confocal Cavity QED (2020) (19)
- GluD2- and Cbln1-mediated competitive interactions shape the dendritic arbors of cerebellar Purkinje cells (2020) (18)
- Feedforward to the Past: The Relation between Neuronal Connectivity, Amplification, and Short-Term Memory (2009) (18)
- Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness (2013) (17)
- The dynamic neural code of the retina for natural scenes (2018) (17)
- Goal-Driven Recurrent Neural Network Models of the Ventral Visual Stream (2021) (17)
- An equivalence between high dimensional Bayes optimal inference and M-estimation (2016) (16)
- Predictive coding in balanced neural networks with noise, chaos and delays (2020) (16)
- MetaMorph: Learning Universal Controllers with Transformers (2022) (14)
- Learning hierarchical categories in deep neural networks (2013) (14)
- Rethinking the limiting dynamics of SGD: modified loss, phase space oscillations, and anomalous diffusion (2021) (14)
- Statistical mechanics of low-rank tensor decomposition (2018) (13)
- Emergent reliability in sensory cortical coding and inter-area communication (2022) (13)
- Survey of Expressivity in Deep Neural Networks (2016) (12)
- E(10) orbifolds (2004) (12)
- A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity (2017) (12)
- The Geometry of Concept Learning (2021) (11)
- Identifying Learning Rules From Neural Network Observables (2020) (11)
- A theory of high dimensional regression with arbitrary correlations between input features and target functions: sample complexity, multiple descent curves and a hierarchy of phase transitions (2021) (9)
- An adaptive low dimensional quasi-Newton sum of functions optimizer (2013) (9)
- E10 Orbifolds (2008) (9)
- Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks (2021) (8)
- Twisted Six Dimensional Gauge Theories on Tori, Matrix Models, and Integrable Systems (2003) (8)
- Neural representational geometry underlies few-shot concept learning (2022) (7)
- How many degrees of freedom do we need to train deep networks: a loss landscape perspective (2021) (7)
- Disentangling with Biological Constraints: A Theory of Functional Cell Types (2022) (6)
- Role of the site of synaptic competition and the balance of learning forces for Hebbian encoding of probabilistic Markov sequences (2015) (6)
- Random projections of random manifolds (2016) (6)
- Noise correlations in neural ensemble activity limit the accuracy of hippocampal spatial representations (2022) (5)
- Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks (2022) (5)
- Time-warped PCA : simultaneous alignment and dimensionality reduction of neural data (2016) (5)
- Universal energy accuracy tradeoffs in nonequilibrium cellular sensing (2020) (5)
- Statistical Mechanics of High-Dimensional Inference (2016) (4)
- Recurrent Connections in the Primate Ventral Visual Stream Mediate a Tradeoff Between Task Performance and Network Size During Core Object Recognition (2022) (4)
- Distinct algorithms for combining landmarks and path integration in medial entorhinal, visual and retrosplenial cortex (2020) (4)
- Learning Dynamics of Deep Networks Admit Low-Rank Tensor Descriptions (2018) (4)
- Quantum mechanics on phase space : geometry and motion of the Wigner distribution (1998) (3)
- Thalamic activity patterns unfolding over multiple time scales predict seizure onset in absence epilepsy (2020) (3)
- Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and parvalbumin-positive interneurons (2021) (3)
- Fast Convolutive Nonnegative Matrix Factorization Through Coordinate and Block Coordinate Updates (2019) (3)
- SemDeDup: Data-efficient learning at web-scale through semantic deduplication (2023) (3)
- Pyret: A Python package for analysis of neurophysiology data (2017) (3)
- Line attractor dynamics in recurrent networks for sentiment classification (2019) (3)
- Optimal noise level for coding with tightly balanced networks of spiking neurons in the presence of transmission delays (2022) (2)
- Convolutional recurrent neural network models of dynamics in higher visual cortex (2018) (2)
- The Asymmetric Maximum Margin Bias of Quasi-Homogeneous Neural Networks (2022) (2)
- An approximate line attractor in the hypothalamus encodes an aggressive state (2023) (2)
- Causal coupling between neural activity, metabolism, and behavior across the Drosophila brain (2020) (2)
- When and why grid cells appear or not in trained path integrators (2022) (2)
- GluD2- and Cbln1-mediated Competitive Synaptogenesis Shapes the Dendritic Arbors of Cerebellar Purkinje Cells (2020) (2)
- Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? (2022) (2)
- Neural networks: from the perceptron to deep nets (2023) (1)
- Distinct in vivo dynamics of excitatory synapses onto cortical pyramidal neurons and inhibitory interneurons (2021) (1)
- Dimension reduction of multi-trial neural data by tensor decomposition (2017) (1)
- NeuroResource Unsupervised Discovery o f Demixed , Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis Graphical (2018) (1)
- Intelligent synapses for multi-task and transfer learning (2017) (1)
- Synaptic balancing: A biologically plausible local learning rule that provably increases neural network noise robustness without sacrificing task performance (2021) (1)
- Statistical Mechanics of High Dimensional Inference Supplementary Material (2016) (1)
- An Energy-Accuracy Tradeoff for Nonequilibrium Receptors (2020) (1)
- Boundary Scattering in 1+1 Dimensions as an Aharanov-Bohm Effect (2005) (1)
- A theory of neural dimensionality, dynamics, and measurement (2016) (1)
- Measuring the dimensionality of behavior (2022) (1)
- Revealing computational mechanisms of retinal prediction via model reduction (2019) (0)
- Supplementary Materials for Cortical layer – specific critical dynamics triggering perception (2019) (0)
- A energy-accuracy tradeoff in the physics of cellular sensing (2017) (0)
- Fibre Bundles and Gauge Theories in Classical Physics: a Uniied Description of Falling Cats, Magnetic Monopoles and Berry's Phase (2008) (0)
- Unrestrained, Freely Moving Rats Population Activity in the Dorsal Subiculum of Analysis of Recordings of Single-Unit Firing and (2015) (0)
- Supplementary Information : A mathematical theory of semantic development in deep neural networks (2019) (0)
- Unmasking the Lottery Ticket Hypothesis: Efficient Adaptive Pruning for Finding Winning Tickets (2022) (0)
- 2 3 O ct 2 01 8 Statistical mechanics of low-rank tensor decomposition (2018) (0)
- What does a deep neural network confidently perceive? The effective dimension of high certainty class manifolds and their low confidence boundaries (2022) (0)
- Principles governing the integration of landmark and self-motion cues in entorhinal cortical codes for navigation (2018) (0)
- Robust and scalable credit assignment without weight symmetry (2020) (0)
- Investigating the learning dynamics of deep neural networks using random matrix theory (2017) (0)
- UCB-PTH-04 / 24 LBNL-56196 E 10 Orbifolds (2014) (0)
- Emergent reliability in sensory cortical coding and inter-area communication (2022) (0)
- Universality and Individuality in Recurrent Neural Networks (2019) (0)
- Supplementary Material : Continual Learning Through Synaptic Intelligence (2017) (0)
- Author response: A saturation hypothesis to explain both enhanced and impaired learning with enhanced plasticity (2016) (0)
- Coherent Ising machines based on optical parametric oscillators (2021) (0)
- Pre-Training on a Data Diet: Identifying Sufficient Examples for Early Training † (2022) (0)
- I NTELLIGENT SYNAPSES FOR MULTITASK AND TRANSFER LEARNING (2017) (0)
- Geometry from algebra: The holographic emergence of spacetime in string theory (2004) (0)
- SERVATION LAWS IN DEEP LEARNING DYNAMICS (2021) (0)
- Designability as a Selection Force? An Analysis of the Yeast Cell Cycle Dynamics. (2007) (0)
- Universality and Individuality in recurrent networks (2019) (0)
- An Evolving-Dynamic Network Activity Approach to Epileptic Seizure Prediction using Machine Learning (2020) (0)
- Control Theoretic Inverse Models in Bird Song Learning: Theory and Experiment (2013) (0)
- Auditory Forebrain Feature Analysis of Natural Sounds in the Songbird (2015) (0)
- Statistical Physics of High Dimensional Inference (2016) (0)
- EEP I NFORMATION P ROPAGATION (2017) (0)
- of Song Sequence Learning II. Temporal Hierarchies and the Learning An Associational Model of Birdsong Sensorimotor (2015) (0)
- Fundamental bounds on the fidelity of sensory cortical coding (2020) (0)
- Notes on Information Theory (2010) (0)
- Quantum Optical Neural Network with Multimode Cavity QED (2020) (0)
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