Spiking neuron network Helmholtz machine
暂无分享,去创建一个
[1] Peter Dayan,et al. Factor Analysis Using Delta-Rule Wake-Sleep Learning , 1997, Neural Computation.
[2] Geoffrey E. Hinton,et al. The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.
[3] P. Berkes,et al. Statistically Optimal Perception and Learning: from Behavior to Neural Representations , 2022 .
[4] Peter Dayan,et al. Recurrent Sampling Models for the Helmholtz Machine , 1999, Neural Computation.
[5] P. J. Sjöström,et al. A Cooperative Switch Determines the Sign of Synaptic Plasticity in Distal Dendrites of Neocortical Pyramidal Neurons , 2006, Neuron.
[6] Jean-Pascal Pfister,et al. Sequence learning with hidden units in spiking neural networks , 2011, NIPS.
[7] R. Jacobs,et al. Experience-dependent visual cue integration based on consistencies between visual and haptic percepts , 2001, Vision Research.
[8] Stefano Panzeri,et al. The Upward Bias in Measures of Information Derived from Limited Data Samples , 1995, Neural Computation.
[9] Aaron R. Seitz,et al. Rapidly learned stimulus expectations alter perception of motion. , 2010, Journal of vision.
[10] E. Bienenstock,et al. Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[11] John E. Stone,et al. OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems , 2010, Computing in Science & Engineering.
[12] Eric Jones,et al. SciPy: Open Source Scientific Tools for Python , 2001 .
[13] Michael R. Waldmann,et al. Causal Reasoning in Rats , 2006, Science.
[14] A. Sittig,et al. Integration of proprioceptive and visual position-information: An experimentally supported model. , 1999, Journal of neurophysiology.
[15] Adam N Sanborn,et al. Rational approximations to rational models: alternative algorithms for category learning. , 2010, Psychological review.
[16] S. Denéve,et al. Neural processing as causal inference , 2011, Current Opinion in Neurobiology.
[17] C. Stevens,et al. Voltage dependence of NMDA-activated macroscopic conductances predicted by single-channel kinetics , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.
[18] William A. Phillips,et al. A Biologically Supported Error-Correcting Learning Rule , 1991, Neural Computation.
[19] Geoffrey E. Hinton,et al. Varieties of Helmholtz Machine , 1996, Neural Networks.
[20] Joshua B. Tenenbaum,et al. Optimal Predictions in Everyday , 2006 .
[21] M. Ernst,et al. Humans integrate visual and haptic information in a statistically optimal fashion , 2002, Nature.
[22] S. Sherman. Tonic and burst firing: dual modes of thalamocortical relay , 2001, Trends in Neurosciences.
[23] H. Abarbanel,et al. Spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. , 2006, Journal of neurophysiology.
[24] Wei Ji Ma,et al. Bayesian inference with probabilistic population codes , 2006, Nature Neuroscience.
[25] J. Tenenbaum,et al. Optimal Predictions in Everyday Cognition , 2006, Psychological science.
[26] M. Steriade. Acetylcholine systems and rhythmic activities during the waking--sleep cycle. , 2004, Progress in brain research.
[27] E. Salinas,et al. Perceptual decision making in less than 30 milliseconds , 2010, Nature Neuroscience.
[28] Richard N Aslin,et al. Bayesian learning of visual chunks by human observers , 2008, Proceedings of the National Academy of Sciences.
[29] S. Nelson,et al. Homeostatic plasticity in the developing nervous system , 2004, Nature Reviews Neuroscience.
[30] K. Svoboda,et al. Experience-dependent structural synaptic plasticity in the mammalian brain , 2009, Nature Reviews Neuroscience.
[31] Wulfram Gerstner,et al. Variational Learning for Recurrent Spiking Networks , 2011, NIPS.
[32] Philip Holmes,et al. Can Monkeys Choose Optimally When Faced with Noisy Stimuli and Unequal Rewards? , 2009, PLoS Comput. Biol..
[33] D. Moore,et al. Early and rapid perceptual learning , 2004, Nature Neuroscience.
[34] Johannes J. Letzkus,et al. Learning Rules for Spike Timing-Dependent Plasticity Depend on Dendritic Synapse Location , 2006, The Journal of Neuroscience.
[35] Feng Qi Han,et al. Reverberation of Recent Visual Experience in Spontaneous Cortical Waves , 2008, Neuron.
[36] Geoffrey E. Hinton,et al. The Helmholtz Machine , 1995, Neural Computation.
[37] J. Snyder,et al. Changes in auditory cortex parallel rapid perceptual learning. , 2006, Cerebral cortex.
[38] Gina G. Turrigiano,et al. Homeostatic Synaptic Plasticity , 2008 .
[39] Chris Eliasmith,et al. Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems , 2004, IEEE Transactions on Neural Networks.
[40] Y. Goda,et al. Homeostatic synaptic plasticity: from single synapses to neural circuits , 2012, Current Opinion in Neurobiology.
[41] D. Burr,et al. The Ventriloquist Effect Results from Near-Optimal Bimodal Integration , 2004, Current Biology.
[42] L. Welberg. Learning and memory: Learning with peaks and troughs , 2013, Nature Reviews Neuroscience.
[43] Michael S Landy,et al. Combining Priors and Noisy Visual Cues in a Rapid Pointing Task , 2006, The Journal of Neuroscience.
[44] Konrad Paul Kording,et al. Bayesian integration in sensorimotor learning , 2004, Nature.
[45] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[46] József Fiser,et al. Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment , 2011, Science.
[47] J. Tenenbaum,et al. Probabilistic models of cognition: exploring representations and inductive biases , 2010, Trends in Cognitive Sciences.
[48] T. Lee. Top-down influence in early visual processing: a Bayesian perspective , 2002, Physiology & Behavior.
[49] Sophie Denève,et al. Bayesian Spiking Neurons I: Inference , 2008, Neural Computation.
[50] P. Dayan. Helmholtz Machines and Wake-Sleep Learning , 2000 .
[51] Jianhua Lin,et al. Divergence measures based on the Shannon entropy , 1991, IEEE Trans. Inf. Theory.
[52] Thomas L. Griffiths,et al. Neural Implementation of Hierarchical Bayesian Inference by Importance Sampling , 2009, NIPS.
[53] G. Tononi,et al. Breakdown of Cortical Effective Connectivity During Sleep , 2005, Science.
[54] Karl J. Friston,et al. Cortical circuits for perceptual inference , 2009, Neural Networks.
[55] B. Katz,et al. Quantal components of the end‐plate potential , 1954, The Journal of physiology.
[56] Shih-Chii Liu,et al. Perceptron learning rule derived from spike-frequency adaptation and spike-time-dependent plasticity , 2010, Proceedings of the National Academy of Sciences.
[57] Edward H. Adelson,et al. Motion illusions as optimal percepts , 2002, Nature Neuroscience.
[58] Wolfgang Maass,et al. Probabilistic Inference in General Graphical Models through Sampling in Stochastic Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[59] Wei Ji Ma,et al. Spiking networks for Bayesian inference and choice , 2008, Current Opinion in Neurobiology.
[60] W. Gerstner,et al. Triplets of Spikes in a Model of Spike Timing-Dependent Plasticity , 2006, The Journal of Neuroscience.
[61] M. Carandini,et al. Population Rate Dynamics and Multineuron Firing Patterns in Sensory Cortex , 2012, The Journal of Neuroscience.
[62] D. Katz,et al. Behavioral states, network states, and sensory response variability. , 2008, Journal of neurophysiology.
[63] Huaiyu Zhu. On Information and Sufficiency , 1997 .
[64] Hermann von Helmholtz,et al. Treatise on Physiological Optics , 1962 .
[65] Rajesh P. N. Rao. Hierarchical Bayesian Inference in Networks of Spiking Neurons , 2004, NIPS.
[66] Sophie Denève,et al. Bayesian Spiking Neurons II: Learning , 2008, Neural Computation.
[67] Eugene M. Izhikevich,et al. Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.
[68] Wolfgang Maass,et al. Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity , 2013, PLoS Comput. Biol..
[69] Wolfgang Maass,et al. Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[70] Walter Bright,et al. The D Programming Language , 2010 .
[71] M. Banks,et al. Visual–Haptic Adaptation Is Determined by Relative Reliability , 2010, The Journal of Neuroscience.
[72] B. McNaughton,et al. Memory trace reactivation in hippocampal and neocortical neuronal ensembles , 2000, Current Opinion in Neurobiology.
[73] Karl J. Friston,et al. Free Energy, Precision and Learning: The Role of Cholinergic Neuromodulation , 2013, The Journal of Neuroscience.
[74] G. Buzsáki,et al. Neuronal Oscillations in Cortical Networks , 2004, Science.
[75] D. Katz,et al. State-dependent modulation of time-varying gustatory responses. , 2006, Journal of neurophysiology.