Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity
暂无分享,去创建一个
Wolfgang Maass | Lars Buesing | Bernhard Nessler | Michael Pfeiffer | Lars Buesing | W. Maass | Bernhard Nessler | Michael Pfeiffer
[1] Wolfgang Maass,et al. STDP enables spiking neurons to detect hidden causes of their inputs , 2009, NIPS.
[2] Stefan Habenschuss,et al. Homeostatic plasticity in Bayesian spiking networks as Expectation Maximization with posterior constraints , 2012, NIPS.
[3] T. Sejnowski,et al. Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons , 2001, Neuroscience.
[4] Lyle N. Long,et al. Hebbian learning with winner take all for spiking neural networks , 2009, 2009 International Joint Conference on Neural Networks.
[5] Jean-Pascal Pfister,et al. Sequence learning with hidden units in spiking neural networks , 2011, NIPS.
[6] P. Berkes,et al. Statistically Optimal Perception and Learning: from Behavior to Neural Representations , 2022 .
[7] D. Debanne,et al. Long-term plasticity of intrinsic excitability: learning rules and mechanisms. , 2003, Learning & memory.
[8] Lyle N. Long,et al. Character Recognition using Spiking Neural Networks , 2007, 2007 International Joint Conference on Neural Networks.
[9] Geoffrey E. Hinton,et al. Reducing the Dimensionality of Data with Neural Networks , 2006, Science.
[10] L. Pinneo. On noise in the nervous system. , 1966, Psychological review.
[11] G. Bi,et al. Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.
[12] W. Singer,et al. Synchronization of neuronal responses in primary visual cortex of monkeys viewing natural images. , 2008, Journal of neurophysiology.
[13] Michael M Merzenich,et al. Lifelong plasticity in the rat auditory cortex: basic mechanisms and role of sensory experience. , 2011, Progress in brain research.
[14] W. Gerstner,et al. Connectivity reflects coding: a model of voltage-based STDP with homeostasis , 2010, Nature Neuroscience.
[15] Steven J. Nowlan,et al. Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures , 1991 .
[16] Wulfram Gerstner,et al. Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning , 2001, Neural Computation.
[17] Masa-aki Sato. Fast Learning of On-line EM Algorithm , 1999 .
[18] Rajesh P. N. Rao. Hierarchical Bayesian Inference in Networks of Spiking Neurons , 2004, NIPS.
[19] Steven J. Nowlan,et al. Maximum Likelihood Competitive Learning , 1989, NIPS.
[20] Pedro M. Domingos,et al. Sum-product networks: A new deep architecture , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).
[21] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[23] D. Feldman. The Spike-Timing Dependence of Plasticity , 2012, Neuron.
[24] Wulfram Gerstner,et al. Predicting spike timing of neocortical pyramidal neurons by simple threshold models , 2006, Journal of Computational Neuroscience.
[25] W. Singer,et al. Frontiers in Integrative Neuroscience Integrative Neuroscience Neural Synchrony in Cortical Networks: History, Concept and Current Status , 2022 .
[26] Robert H. Cudmore,et al. Long-term potentiation of intrinsic excitability in LV visual cortical neurons. , 2004, Journal of neurophysiology.
[27] L. Abbott,et al. Synaptic plasticity: taming the beast , 2000, Nature Neuroscience.
[28] Michael I. Jordan,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1994, Neural Computation.
[29] Michael Collins,et al. EM Algorithm , 2010, Encyclopedia of Machine Learning.
[30] J. Pfister,et al. A triplet spike-timing–dependent plasticity model generalizes the Bienenstock–Cooper–Munro rule to higher-order spatiotemporal correlations , 2011, Proceedings of the National Academy of Sciences.
[31] N. Brunel,et al. Calcium-based plasticity model explains sensitivity of synaptic changes to spike pattern, rate, and dendritic location , 2012, Proceedings of the National Academy of Sciences.
[32] P. J. Sjöström,et al. Rate, Timing, and Cooperativity Jointly Determine Cortical Synaptic Plasticity , 2001, Neuron.
[33] Timothée Masquelier,et al. Competitive STDP-Based Spike Pattern Learning , 2009, Neural Computation.
[34] Wolfgang Maass,et al. Hebbian Learning of Bayes Optimal Decisions , 2008, NIPS.
[35] Mu-ming Poo,et al. Spike Train Timing-Dependent Associative Modification of Hippocampal CA3 Recurrent Synapses by Mossy Fibers , 2004, Neuron.
[36] W. Singer,et al. Distributed Fading Memory for Stimulus Properties in the Primary Visual Cortex , 2009, PLoS biology.
[37] S. Grillner,et al. Microcircuits : the interface between neurons and global brain function , 2006 .
[38] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[39] Gustavo Deco,et al. Oscillations, Phase-of-Firing Coding, and Spike Timing-Dependent Plasticity: An Efficient Learning Scheme , 2009, The Journal of Neuroscience.
[40] Nikola T. Markov,et al. Weight Consistency Specifies Regularities of Macaque Cortical Networks , 2010, Cerebral cortex.
[41] L. Abbott,et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.
[42] Alexander S. Ecker,et al. Decorrelated Neuronal Firing in Cortical Microcircuits , 2010, Science.
[43] Wolfgang Maass,et al. On the Computational Power of Winner-Take-All , 2000, Neural Computation.
[44] Wolfgang Maass,et al. Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[45] Geoffrey E. Hinton,et al. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants , 1998, Learning in Graphical Models.
[46] Michael H. Herzog,et al. Effects of grouping in contextual modulation , 2002, Nature.
[47] S. Thorpe,et al. Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains , 2008, PloS one.
[48] Rajesh P. N. Rao. Neural Models of Bayesian Belief Propagation , 2006 .
[49] Mu-Ming Poo,et al. Frontiers in Synaptic Neuroscience Synaptic Neuroscience , 2022 .
[50] Christof Koch,et al. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .
[51] Wei Ji Ma,et al. Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.
[52] D. Fitzpatrick,et al. The development of direction selectivity in ferret visual cortex requires early visual experience , 2006, Nature Neuroscience.
[53] Jean-Pascal Pfister,et al. Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning , 2005, Neural Computation.
[54] Jörg Lücke,et al. Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin? , 2012, PLoS Comput. Biol..
[55] Russell Greiner,et al. Computational learning theory and natural learning systems: Volume IV: making learning systems practical , 1997, COLT 1997.
[56] J. Tenenbaum,et al. Optimal Predictions in Everyday Cognition , 2006, Psychological science.
[57] KochChristof,et al. A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 1998 .
[58] Wulfram Gerstner,et al. Spiking Neuron Models , 2002 .
[59] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[60] Charles Kemp,et al. Bayesian models of cognition , 2008 .
[61] Richard Hans Robert Hahnloser,et al. Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit , 2000, Nature.
[62] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[63] H. Kushner,et al. Stochastic Approximation and Recursive Algorithms and Applications , 2003 .
[64] Klaus Pawelzik,et al. Spike timing-dependent plasticity as dynamic filter , 2010, NIPS.
[65] C. Gilbert,et al. The Neural Basis of Perceptual Learning , 2001, Neuron.
[66] R. Douglas,et al. Neuronal circuits of the neocortex. , 2004, Annual review of neuroscience.
[67] D. Rubin,et al. Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .
[68] Shin Ishii,et al. On-line EM Algorithm for the Normalized Gaussian Network , 2000, Neural Computation.
[69] Shih-Chii Liu,et al. Computation with Spikes in a Winner-Take-All Network , 2009, Neural Computation.
[70] Geoffrey E. Hinton,et al. Deep Boltzmann Machines , 2009, AISTATS.
[71] P. Pavlidis,et al. Pair Recordings Reveal All-Silent Synaptic Connections and the Postsynaptic Expression of Long-Term Potentiation , 2001, Neuron.
[72] D. Long. Probabilistic Models of the Brain. , 2002 .
[73] Michael J. Goard,et al. Basal Forebrain Activation Enhances Cortical Coding of Natural Scenes , 2009, Nature Neuroscience.
[74] Eugenio Rodriguez,et al. Neural synchrony and the development of cortical networks , 2010, Trends in Cognitive Sciences.
[75] David S. Greenberg,et al. Spatial Organization of Neuronal Population Responses in Layer 2/3 of Rat Barrel Cortex , 2007, The Journal of Neuroscience.
[76] Steve B. Furber,et al. Modeling Spiking Neural Networks on SpiNNaker , 2010, Computing in Science & Engineering.
[77] R. Kempter,et al. Hebbian learning and spiking neurons , 1999 .
[78] Gert Cauwenberghs,et al. Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.
[79] Peter Dayan,et al. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems , 2001 .
[80] N. Chater,et al. Précis of Bayesian Rationality: The Probabilistic Approach to Human Reasoning , 2009, Behavioral and Brain Sciences.
[81] Michael Okun,et al. Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities , 2008, Nature Neuroscience.
[82] Sophie Denève,et al. Bayesian Spiking Neurons I: Inference , 2008, Neural Computation.
[83] G. Turrigiano. Too many cooks? Intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. , 2011, Annual review of neuroscience.
[84] L. Abbott,et al. Cortical Development and Remapping through Spike Timing-Dependent Plasticity , 2001, Neuron.
[85] David Zipser,et al. Feature Discovery by Competive Learning , 1986, Cogn. Sci..
[86] L. Merabet,et al. Neural reorganization following sensory loss: the opportunity of change , 2010, Nature Reviews Neuroscience.
[87] H. Sompolinsky,et al. Time-Warp–Invariant Neuronal Processing , 2009, PLoS biology.
[88] Konrad Paul Kording,et al. Bayesian integration in sensorimotor learning , 2004, Nature.
[89] Geoffrey E. Hinton,et al. Generative models for discovering sparse distributed representations. , 1997, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.
[90] Johannes Schemmel,et al. Modeling Synaptic Plasticity within Networks of Highly Accelerated I&F Neurons , 2007, 2007 IEEE International Symposium on Circuits and Systems.
[91] Marc'Aurelio Ranzato,et al. Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.
[92] Luca Maria Gambardella,et al. Deep, Big, Simple Neural Nets for Handwritten Digit Recognition , 2010, Neural Computation.
[93] Jochen Triesch,et al. Independent Component Analysis in Spiking Neurons , 2010, PLoS Comput. Biol..
[94] Wolfgang Maass,et al. Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[95] Wulfram Gerstner,et al. Phenomenological models of synaptic plasticity based on spike timing , 2008, Biological Cybernetics.
[96] Wolfgang Jank,et al. The EM Algorithm, Its Randomized Implementation and Global Optimization: Some Challenges and Opportunities for Operations Research , 2006 .
[97] Michael I. Jordan,et al. Mixture models for learning from incomplete data , 1997, Annual Conference Computational Learning Theory.
[98] David J. Field,et al. How Close Are We to Understanding V1? , 2005, Neural Computation.
[99] R. Douglas,et al. Stereotypical Bouton Clustering of Individual Neurons in Cat Primary Visual Cortex , 2007, The Journal of Neuroscience.
[100] R. Desimone. Face-Selective Cells in the Temporal Cortex of Monkeys , 1991, Journal of Cognitive Neuroscience.
[101] Sophie Denève,et al. Bayesian Spiking Neurons II: Learning , 2008, Neural Computation.
[102] Matthieu Gilson,et al. STDP Allows Fast Rate-Modulated Coding with Poisson-Like Spike Trains , 2011, PLoS Comput. Biol..
[103] W. Gerstner,et al. Generalized Bienenstock-Cooper-Munro rule for spiking neurons that maximizes information transmission. , 2005, Proceedings of the National Academy of Sciences of the United States of America.
[104] Ueli Rutishauser,et al. State-Dependent Computation Using Coupled Recurrent Networks , 2008, Neural Computation.
[105] R. Malinow,et al. Direct measurement of quantal changes underlying long-term potentiation in CA1 hippocampus , 1992, Neuron.
[106] W. Marsden. I and J , 2012 .
[107] C. Gilbert,et al. Perceptual learning and adult cortical plasticity , 2009, The Journal of physiology.
[108] Wolfgang Maass,et al. Reward-Modulated Hebbian Learning of Decision Making , 2010, Neural Computation.
[109] R. Yuste,et al. Dense Inhibitory Connectivity in Neocortex , 2011, Neuron.
[110] Rajesh P. N. Rao,et al. Bayesian brain : probabilistic approaches to neural coding , 2006 .
[111] Peter Dayan,et al. Probabilistic Computation in Spiking Populations , 2004, NIPS.
[112] Y. Dan,et al. Spike timing-dependent plasticity: a Hebbian learning rule. , 2008, Annual review of neuroscience.
[113] Luca Maria Gambardella,et al. Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition , 2010, ArXiv.
[114] Y. Dan,et al. Spike Timing-Dependent Plasticity of Neural Circuits , 2004, Neuron.
[115] M. Stryker,et al. Development and Plasticity of the Primary Visual Cortex , 2012, Neuron.
[116] Robert A. Jacobs,et al. Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.
[117] M. Carandini,et al. Normalization as a canonical neural computation , 2011, Nature Reviews Neuroscience.
[118] Y. Dan,et al. Spike-timing-dependent synaptic modification induced by natural spike trains , 2002, Nature.
[119] D. O. Hebb,et al. The organization of behavior , 1988 .
[120] Wulfram Gerstner,et al. Variational Learning for Recurrent Spiking Networks , 2011, NIPS.