Bidirectional Learning in Recurrent Neural Networks Using Equilibrium Propagation
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[1] J. Fritz,et al. Differential Dynamic Plasticity of A1 Receptive Fields during Multiple Spectral Tasks , 2005, The Journal of Neuroscience.
[2] T. Sejnowski,et al. Neurocomputational models of working memory , 2000, Nature Neuroscience.
[3] Joel Z. Leibo,et al. How Important Is Weight Symmetry in Backpropagation? , 2015, AAAI.
[4] 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.
[5] Rob Fergus,et al. Visualizing and Understanding Convolutional Networks , 2013, ECCV.
[6] R. O’Reilly. Six principles for biologically based computational models of cortical cognition , 1998, Trends in Cognitive Sciences.
[7] Wolfgang Maass,et al. Emergence of complex computational structures from chaotic neural networks through reward-modulated Hebbian learning. , 2014, Cerebral cortex.
[8] Yoshua Bengio,et al. Generative Adversarial Nets , 2014, NIPS.
[9] D. G. Stork,et al. Is backpropagation biologically plausible? , 1989, International 1989 Joint Conference on Neural Networks.
[10] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[11] Arild Nøkland,et al. Direct Feedback Alignment Provides Learning in Deep Neural Networks , 2016, NIPS.
[12] A. Gittis,et al. Intrinsic and synaptic plasticity in the vestibular system , 2006, Current Opinion in Neurobiology.
[13] L. Abbott,et al. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity , 2000, Nature Neuroscience.
[14] Yoshua Bengio,et al. Towards Biologically Plausible Deep Learning , 2015, ArXiv.
[15] Xiaohui Xie,et al. Equivalence of Backpropagation and Contrastive Hebbian Learning in a Layered Network , 2003, Neural Computation.
[16] Peter Lakatos,et al. Dynamics of Active Sensing and perceptual selection , 2010, Current Opinion in Neurobiology.
[17] Ilya Sutskever,et al. On the Convergence Properties of Contrastive Divergence , 2010, AISTATS.
[18] David E. Rumelhart,et al. Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks , 1989, Neural Computation.
[19] Yoshua Bengio,et al. Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.
[20] P. Dean,et al. The importance of Marr’s three levels of analysis for understanding cerebellar function , 2016 .
[21] Nicholas Furl,et al. Structural and effective connectivity reveals potential network-based influences on category-sensitive visual areas , 2015, Front. Hum. Neurosci..
[22] Yoshua Bengio,et al. Difference Target Propagation , 2014, ECML/PKDD.
[23] Miguel Á. Carreira-Perpiñán,et al. On Contrastive Divergence Learning , 2005, AISTATS.
[24] Pierre Baldi,et al. Contrastive Learning and Neural Oscillations , 1991, Neural Computation.
[25] Karl J. Friston,et al. Dynamic representations and generative models of brain function , 2001, Brain Research Bulletin.
[26] Yoshua Bengio,et al. Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation , 2016, Front. Comput. Neurosci..
[27] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[28] Colin J. Akerman,et al. Random synaptic feedback weights support error backpropagation for deep learning , 2016, Nature Communications.
[29] Jochen Triesch,et al. A Gradient Rule for the Plasticity of a Neuron's Intrinsic Excitability , 2005, ICANN.
[30] Andrzej Kasiński,et al. Comparison of supervised learning methods for spike time coding in spiking neural networks , 2006 .
[31] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[32] John J. Hopfield,et al. Neural networks and physical systems with emergent collective computational abilities , 1999 .
[33] Razvan Pascanu,et al. Learning Algorithms for the Classification Restricted Boltzmann Machine , 2012, J. Mach. Learn. Res..
[34] Jochen J. Steil,et al. Improving reservoirs using intrinsic plasticity , 2008, Neurocomputing.
[35] Terrence J. Sejnowski,et al. Unsupervised Learning , 2018, Encyclopedia of GIS.
[36] Christof Koch,et al. The role of single neurons in information processing , 2000, Nature Neuroscience.
[37] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[38] Razvan Pascanu,et al. On the difficulty of training recurrent neural networks , 2012, ICML.
[39] Steven A Prescott,et al. Spike-Rate Coding and Spike-Time Coding Are Affected Oppositely by Different Adaptation Mechanisms , 2008, The Journal of Neuroscience.
[40] Herbert Jaeger,et al. Adaptive Nonlinear System Identification with Echo State Networks , 2002, NIPS.
[41] J. DiCarlo,et al. Using goal-driven deep learning models to understand sensory cortex , 2016, Nature Neuroscience.
[42] P J Webros. BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .
[43] W. Gerstner,et al. Spike-Timing-Dependent Plasticity: A Comprehensive Overview , 2012, Front. Syn. Neurosci..
[44] Michael I. Jordan,et al. Deterministic Boltzmann Learning Performs Steepest Descent in Weight-Space , 2001 .
[45] Jürgen Schmidhuber,et al. Learning to forget: continual prediction with LSTM , 1999 .