Backpropagation and the brain
暂无分享,去创建一个
Adam Santoro | Colin J. Akerman | Geoffrey Hinton | Timothy P. Lillicrap | Luke Marris | T. Lillicrap | Adam Santoro | Luke Marris | C. Akerman | Geoffrey Hinton | Geoffrey F Hinton
[1] PHARMACOLOGY AND NERVE ENDINGS , 1934 .
[2] H. Dale. Pharmacology and Nerve-Endings , 1935 .
[3] V. Mountcastle. Modality and topographic properties of single neurons of cat's somatic sensory cortex. , 1957, Journal of neurophysiology.
[4] D Marr,et al. Simple memory: a theory for archicortex. , 1971, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[5] T. Bliss,et al. Long‐lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path , 1973, The Journal of physiology.
[6] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[7] N. Osborne. Is Dale's principle valid? , 1979, Trends in Neurosciences.
[8] P. Andersen,et al. Possible mechanisms for long‐lasting potentiation of synaptic transmission in hippocampal slices from guinea‐pigs. , 1980, The Journal of physiology.
[9] T. O'donohue,et al. On the 50th anniversary of Dale's law: multiple neurotransmitter neurons , 1985 .
[10] Geoffrey E. Hinton,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cogn. Sci..
[11] R. Desimone,et al. Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.
[12] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[13] Geoffrey E. Hinton,et al. Learning internal representations by error propagation , 1986 .
[14] Y. L. Cun. Learning Process in an Asymmetric Threshold Network , 1986 .
[15] Paul Smolensky,et al. Information processing in dynamical systems: foundations of harmony theory , 1986 .
[16] Geoffrey E. Hinton,et al. Learning representations of back-propagation errors , 1986 .
[17] Stephen Grossberg,et al. Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987, Cogn. Sci..
[18] B. C. Motter,et al. Common and differential effects of attentive fixation on the excitability of parietal and prestriate (V4) cortical visual neurons in the macaque monkey , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[19] Stephen Grossberg,et al. From Interactive Activation to Adaptive Resonance , 1987 .
[20] Pineda,et al. Generalization of back-propagation to recurrent neural networks. , 1987, Physical review letters.
[21] Stephen Grossberg. Competitive Learning: From Interactive Activation to Adaptive Resonance , 1987 .
[22] Yann LeCun,et al. Modeles connexionnistes de l'apprentissage , 1987 .
[23] Terrence J. Sejnowski,et al. A Learning Algorithm for Boltzmann Machines , 1985, Cognitive Sciences.
[24] Fernando J. Pineda,et al. Dynamics and architecture for neural computation , 1988, J. Complex..
[25] H. Spitzer,et al. Increased attention enhances both behavioral and neuronal performance. , 1988, Science.
[26] Richard A. Andersen,et al. A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons , 1988, Nature.
[27] D. G. Stork,et al. Is backpropagation biologically plausible? , 1989, International 1989 Joint Conference on Neural Networks.
[28] Kevan A. C. Martin,et al. A Canonical Microcircuit for Neocortex , 1989, Neural Computation.
[29] Francis Crick,et al. The recent excitement about neural networks , 1989, Nature.
[30] Michael I. Jordan,et al. A more biologically plausible learning rule for neural networks. , 1991, Proceedings of the National Academy of Sciences of the United States of America.
[31] Javier R. Movellan,et al. Contrastive Hebbian Learning in the Continuous Hopfield Model , 1991 .
[32] M. Mignard,et al. Paths of information flow through visual cortex. , 1991, Science.
[33] Marwan A. Jabri,et al. Summed Weight Neuron Perturbation: An O(N) Improvement Over Weight Perturbation , 1992, NIPS.
[34] J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .
[35] D. Nelkin. In the name of genetics , 1993, Nature.
[36] B. C. Motter. Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. , 1993, Journal of neurophysiology.
[37] John Duncan,et al. A neural basis for visual search in inferior temporal cortex , 1993, Nature.
[38] D. Signorini,et al. Neural networks , 1995, The Lancet.
[39] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[40] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[41] Christopher M. Bishop,et al. Neural networks for pattern recognition , 1995 .
[42] J. Maunsell,et al. Attentional modulation of visual motion processing in cortical areas MT and MST , 1996, Nature.
[43] Wulfram Gerstner,et al. A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.
[44] Randall C. O'Reilly,et al. Biologically Plausible Error-Driven Learning Using Local Activation Differences: The Generalized Recirculation Algorithm , 1996, Neural Computation.
[45] Peter Dayan,et al. A Neural Substrate of Prediction and Reward , 1997, Science.
[46] Gaetan Libert,et al. Emergence of clusters in the hidden layer of a dynamic recurrent neural network , 1997, Biological Cybernetics.
[47] L. Abbott,et al. Synaptic Depression and Cortical Gain Control , 1997, Science.
[48] H. Markram,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.
[49] Dorothea Heiss-Czedik,et al. An Introduction to Genetic Algorithms. , 1997, Artificial Life.
[50] R. Desimone,et al. Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. , 1997, Journal of neurophysiology.
[51] B. Sakmann,et al. A new cellular mechanism for coupling inputs arriving at different cortical layers , 1999, Nature.
[52] Carrie J. McAdams,et al. Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4 , 1999, The Journal of Neuroscience.
[53] C. Gilbert,et al. Attention Modulates Contextual Influences in the Primary Visual Cortex of Alert Monkeys , 1999, Neuron.
[54] J Duncan,et al. Responses of neurons in macaque area V4 during memory-guided visual search. , 2001, Cerebral cortex.
[55] J. Bullier,et al. Feedforward and feedback connections between areas V1 and V2 of the monkey have similar rapid conduction velocities. , 2001, Journal of neurophysiology.
[56] Frank van der Velde,et al. From artificial neural networks to spiking neuron populations and back again , 2001, Neural Networks.
[57] R. Guillery,et al. Thalamic Relay Functions and Their Role in Corticocortical Communication Generalizations from the Visual System , 2002, Neuron.
[58] Frances S. Chance,et al. Gain Modulation from Background Synaptic Input , 2002, Neuron.
[59] G. Elston. Cortex, cognition and the cell: new insights into the pyramidal neuron and prefrontal function. , 2003, Cerebral cortex.
[60] Frank Tong,et al. Cognitive neuroscience: Primary visual cortex and visual awareness , 2003, Nature Reviews Neuroscience.
[61] Tai Sing Lee,et al. Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.
[62] S. Kosslyn,et al. When is early visual cortex activated during visual mental imagery? , 2003, Psychological bulletin.
[63] R. Desimone,et al. Interacting Roles of Attention and Visual Salience in V4 , 2003, Neuron.
[64] H. Seung,et al. Learning in Spiking Neural Networks by Reinforcement of Stochastic Synaptic Transmission , 2003, Neuron.
[65] Xiaohui Xie,et al. Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network , 2003, Neural Computation.
[66] E. Oztaş. Neuronal tracing , 2003 .
[67] Geoffrey E. Hinton. The ups and downs of Hebb synapses. , 2003 .
[68] S. Hochstein,et al. The reverse hierarchy theory of visual perceptual learning , 2004, Trends in Cognitive Sciences.
[69] C. Hansel,et al. Bidirectional Parallel Fiber Plasticity in the Cerebellum under Climbing Fiber Control , 2004, Neuron.
[70] Ronald J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[71] Konrad P. Körding,et al. Supervised and Unsupervised Learning with Two Sites of Synaptic Integration , 2001, Journal of Computational Neuroscience.
[72] A. Burkhalter,et al. Conserved patterns of cortico-cortical connections define areal hierarchy in rat visual cortex , 2004, Experimental Brain Research.
[73] Bartlett W. Mel,et al. Computational subunits in thin dendrites of pyramidal cells , 2004, Nature Neuroscience.
[74] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.
[75] Xiaohui Xie,et al. Learning Curves for Stochastic Gradient Descent in Linear Feedforward Networks , 2003, Neural Computation.
[76] Urit Gordon,et al. Plasticity Compartments in Basal Dendrites of Neocortical Pyramidal Neurons , 2006, The Journal of Neuroscience.
[77] C. Gilbert,et al. Contour Saliency in Primary Visual Cortex , 2006, Neuron.
[78] P. J. Sjöström,et al. A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons , 2006, Neuron.
[79] Judit K. Makara,et al. Compartmentalized dendritic plasticity and input feature storage in neurons , 2008, Nature.
[80] Chris Eliasmith,et al. Solving the Problem of Negative Synaptic Weights in Cortical Models , 2008, Neural Computation.
[81] Nicolas Pinto,et al. Why is Real-World Visual Object Recognition Hard? , 2008, PLoS Comput. Biol..
[82] P. Somogyi,et al. Neuronal Diversity and Temporal Dynamics: The Unity of Hippocampal Circuit Operations , 2008, Science.
[83] Timothy P. Lillicrap,et al. Sensitivity Derivatives for Flexible Sensorimotor Learning , 2008, Neural Computation.
[84] K. Harris. Stability of the fittest: organizing learning through retroaxonal signals , 2008, Trends in Neurosciences.
[85] J. Kwag,et al. The timing of external input controls the sign of plasticity at local synapses , 2009, Nature Neuroscience.
[86] M. Häusser,et al. Dendritic Discrimination of Temporal Input Sequences in Cortical Neurons , 2010, Science.
[87] B. Sakmann,et al. Dimensions of a Projection Column and Architecture of VPM and POm Axons in Rat Vibrissal Cortex , 2010, Cerebral cortex.
[88] L ChintaVenkateswararao,et al. Adaptive optimal-control algorithms for brainlike networks. , 2010 .
[89] Eero P. Simoncelli,et al. Metamers of the ventral stream , 2011, Nature Neuroscience.
[90] S. Sherman,et al. Synaptic Properties of Corticocortical Connections between the Primary and Secondary Visual Cortical Areas in the Mouse , 2011, The Journal of Neuroscience.
[91] M. Häusser,et al. Synaptic Integration Gradients in Single Cortical Pyramidal Cell Dendrites , 2011, Neuron.
[92] S. Sherman,et al. Properties of the thalamic projection from the posterior medial nucleus to primary and secondary somatosensory cortices in the mouse , 2011, Proceedings of the National Academy of Sciences.
[93] H. Bridge,et al. Vivid visual mental imagery in the absence of the primary visual cortex , 2011, Journal of Neurology.
[94] R W Guillery,et al. Distinct functions for direct and transthalamic corticocortical connections. , 2011, Journal of neurophysiology.
[95] Karl J. Friston,et al. Canonical Microcircuits for Predictive Coding , 2012, Neuron.
[96] T. Lillicrap,et al. Preference Distributions of Primary Motor Cortex Neurons Reflect Control Solutions Optimized for Limb Biomechanics , 2013, Neuron.
[97] Henry Kennedy,et al. The importance of being hierarchical , 2013, Current Opinion in Neurobiology.
[98] Jan Peters,et al. A Survey on Policy Search for Robotics , 2013, Found. Trends Robotics.
[99] C. Gilbert,et al. Top-down influences on visual processing , 2013, Nature Reviews Neuroscience.
[100] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[101] Erich Elsen,et al. Deep Speech: Scaling up end-to-end speech recognition , 2014, ArXiv.
[102] Ha Hong,et al. Performance-optimized hierarchical models predict neural responses in higher visual cortex , 2014, Proceedings of the National Academy of Sciences.
[103] Yan Yang,et al. Duration of complex-spikes grades Purkinje cell plasticity and cerebellar motor learning , 2014, Nature.
[104] Yoshua Bengio,et al. How Auto-Encoders Could Provide Credit Assignment in Deep Networks via Target Propagation , 2014, ArXiv.
[105] Daniel L. K. Yamins,et al. Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition , 2014, PLoS Comput. Biol..
[106] W. Senn,et al. Learning by the Dendritic Prediction of Somatic Spiking , 2014, Neuron.
[107] Daniel Cownden,et al. Random feedback weights support learning in deep neural networks , 2014, ArXiv.
[108] Allan R. Jones,et al. A mesoscale connectome of the mouse brain , 2014, Nature.
[109] Nikolaus Kriegeskorte,et al. Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..
[110] Adam Binch,et al. Perception as Bayesian Inference , 2014 .
[111] S. Manita,et al. A Top-Down Cortical Circuit for Accurate Sensory Perception , 2015, Neuron.
[112] Alexander S. Ecker,et al. Principles of connectivity among morphologically defined cell types in adult neocortex , 2015, Science.
[113] Nikolaus Kriegeskorte,et al. Deep neural networks: a new framework for modelling biological vision and brain information processing , 2015, bioRxiv.
[114] Susumu Tonegawa,et al. Conjunctive input processing drives feature selectivity in hippocampal CA1 neurons , 2015, Nature Neuroscience.
[115] Yoshua Bengio,et al. Difference Target Propagation , 2014, ECML/PKDD.
[116] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[117] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[118] Colin J. Akerman,et al. Random synaptic feedback weights support error backpropagation for deep learning , 2016, Nature Communications.
[119] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[120] Yonghui Wu,et al. Exploring the Limits of Language Modeling , 2016, ArXiv.
[121] Koray Kavukcuoglu,et al. Pixel Recurrent Neural Networks , 2016, ICML.
[122] Shimon Ullman,et al. Atoms of recognition in human and computer vision , 2016, Proceedings of the National Academy of Sciences.
[123] Yoshua Bengio,et al. Towards a Biologically Plausible Backprop , 2016, ArXiv.
[124] Timothy P. Lillicrap,et al. Deep learning with segregated dendrites , 2016 .
[125] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[126] Georg B. Keller,et al. Mismatch Receptive Fields in Mouse Visual Cortex , 2016, Neuron.
[127] Walter Senn,et al. Somato-dendritic Synaptic Plasticity and Error-backpropagation in Active Dendrites , 2016, PLoS Comput. Biol..
[128] Heiga Zen,et al. WaveNet: A Generative Model for Raw Audio , 2016, SSW.
[129] Joel Z. Leibo,et al. How Important Is Weight Symmetry in Backpropagation? , 2015, AAAI.
[130] Kevin Waugh,et al. DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker , 2017, ArXiv.
[131] R. D. D'Souza,et al. A Laminar Organization for Selective Cortico-Cortical Communication , 2017, Front. Neuroanat..
[132] Yoshua Bengio,et al. Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation , 2016, Front. Comput. Neurosci..
[133] Katie C. Bittner,et al. Behavioral time scale synaptic plasticity underlies CA1 place fields , 2017, Science.
[134] Timothy P Lillicrap,et al. Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights , 2017, Neural Computation.
[135] Kevin Waugh,et al. DeepStack: Expert-level artificial intelligence in heads-up no-limit poker , 2017, Science.
[136] Eric T. Shea-Brown,et al. Dynamic representation of partially occluded objects in primate prefrontal and visual cortex , 2017, eLife.
[137] Rafal Bogacz,et al. An Approximation of the Error Backpropagation Algorithm in a Predictive Coding Network with Local Hebbian Synaptic Plasticity , 2017, Neural Computation.
[138] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[139] Timothy P Lillicrap,et al. Towards deep learning with segregated dendrites , 2016, eLife.
[140] Odelia Schwartz,et al. Faculty of 1000 evaluation for Deep neural networks: A new framework for modeling biological vision and brain information processing. , 2017 .
[141] Daniel L. K. Yamins,et al. A Task-Optimized Neural Network Replicates Human Auditory Behavior, Predicts Brain Responses, and Reveals a Cortical Processing Hierarchy , 2018, Neuron.
[142] Pieter R. Roelfsema,et al. Control of synaptic plasticity in deep cortical networks , 2018, Nature Reviews Neuroscience.
[143] Richard Naud,et al. Sparse bursts optimize information transmission in a multiplexed neural code , 2018, Proceedings of the National Academy of Sciences.
[144] Elias B. Issa,et al. Neural dynamics at successive stages of the ventral visual stream are consistent with hierarchical error signals , 2018, eLife.
[145] Aaron R. Seitz,et al. Deep Neural Networks for Modeling Visual Perceptual Learning , 2018, The Journal of Neuroscience.
[146] Yoshua Bengio,et al. Dendritic error backpropagation in deep cortical microcircuits , 2017, ArXiv.
[147] Allan R. Jones,et al. Shared and distinct transcriptomic cell types across neocortical areas , 2018, Nature.
[148] L. F. Abbott,et al. Feedback alignment in deep convolutional networks , 2018, ArXiv.
[149] Timothy Lillicrap,et al. Using Weight Mirrors to Improve Feedback Alignment , 2019 .
[150] Timothy P Lillicrap,et al. Dendritic solutions to the credit assignment problem , 2019, Current Opinion in Neurobiology.
[151] Tomaso A. Poggio,et al. Biologically-plausible learning algorithms can scale to large datasets , 2018, ICLR.
[152] James C. R. Whittington,et al. Theories of Error Back-Propagation in the Brain , 2019, Trends in Cognitive Sciences.
[153] Yali Amit,et al. Deep Learning With Asymmetric Connections and Hebbian Updates , 2018, Front. Comput. Neurosci..
[154] Lasse Becker-Czarnetzki. Report on DeepStack Expert-Level Artificial Intelligence in Heads-Up No-Limit Poker , 2019 .
[155] L. Abbott,et al. Continual Learning in a Multi-Layer Network of an Electric Fish , 2019, Cell.
[156] Leena E Williams,et al. Higher-Order Thalamocortical Inputs Gate Synaptic Long-Term Potentiation via Disinhibition , 2018, Neuron.