STDP as presynaptic activity times rate of change of postsynaptic activity
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Yoshua Bengio | Asja Fischer | Saizheng Zhang | Thomas Mesnard | Yuhuai Wu | Yoshua Bengio | Yuhuai Wu | Saizheng Zhang | Asja Fischer | T. Mesnard
[1] G. Bi,et al. Synaptic modification by correlated activity: Hebb's postulate revisited. , 2001, Annual review of neuroscience.
[2] Nathan Intrator,et al. Objective function formulation of the BCM theory of visual cortical plasticity: Statistical connections, stability conditions , 1992, Neural Networks.
[3] Xiaohui Xie,et al. Spike-based Learning Rules and Stabilization of Persistent Neural Activity , 1999, NIPS.
[4] W. Precht. The synaptic organization of the brain G.M. Shepherd, Oxford University Press (1975). 364 pp., £3.80 (paperback) , 1976, Neuroscience.
[5] 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.
[6] Wulfram Gerstner,et al. A neuronal learning rule for sub-millisecond temporal coding , 1996, Nature.
[7] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[8] József Fiser,et al. Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment , 2011, Science.
[9] 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.
[10] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[11] Pascal Vincent,et al. A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.
[12] J J Hopfield,et al. Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.
[13] Daniel Cownden,et al. Random feedback weights support learning in deep neural networks , 2014, ArXiv.
[14] H. Markram,et al. Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997, Science.
[15] Yoshua Bengio,et al. Early Inference in Energy-Based Models Approximates Back-Propagation , 2015, ArXiv.
[16] Sanjeev Arora,et al. Why are deep nets reversible: A simple theory, with implications for training , 2015, ArXiv.
[17] Karl J. Friston,et al. Free-energy and the brain , 2007, Synthese.
[18] Y. Dan,et al. Spike-timing-dependent synaptic modification induced by natural spike trains , 2002, Nature.
[19] Yoshua Bengio,et al. Towards a Biologically Plausible Backprop , 2016, ArXiv.
[20] Mark C. W. van Rossum,et al. Stable Hebbian Learning from Spike Timing-Dependent Plasticity , 2000, The Journal of Neuroscience.
[21] D. Feldman. The Spike-Timing Dependence of Plasticity , 2012, Neuron.
[22] Nando de Freitas,et al. An Introduction to MCMC for Machine Learning , 2004, Machine Learning.
[23] R. J. Williams,et al. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning , 2004, Machine Learning.
[24] W. Gerstner,et al. Spike-Timing-Dependent Plasticity: A Comprehensive Overview , 2012, Front. Syn. Neurosci..
[25] Wulfram Gerstner,et al. Stochastic variational learning in recurrent spiking networks , 2014, Front. Comput. Neurosci..
[26] Geoffrey E. Hinton,et al. Learning Representations by Recirculation , 1987, NIPS.
[27] Pascal Vincent,et al. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..
[28] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[29] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[30] 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.
[31] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, ArXiv.
[32] Yoshua Bengio,et al. Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation , 2016, Front. Comput. Neurosci..
[33] Wulfram Gerstner,et al. Phenomenological models of synaptic plasticity based on spike timing , 2008, Biological Cybernetics.
[34] Ila R Fiete,et al. Gradient learning in spiking neural networks by dynamic perturbation of conductances. , 2006, Physical review letters.