暂无分享,去创建一个
[1] Anthony S. Maida,et al. Training a Hidden Markov Model with a Bayesian Spiking Neural Network , 2016, Journal of Signal Processing Systems.
[2] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .
[3] Wolfgang Maass,et al. STDP enables spiking neurons to detect hidden causes of their inputs , 2009, NIPS.
[4] Tobi Delbruck,et al. Real-time classification and sensor fusion with a spiking deep belief network , 2013, Front. Neurosci..
[5] Kendra S. Burbank. Mirrored STDP Implements Autoencoder Learning in a Network of Spiking Neurons , 2015, PLoS Comput. Biol..
[6] Wolfgang Maass,et al. Emergence of Dynamic Memory Traces in Cortical Microcircuit Models through STDP , 2013, The Journal of Neuroscience.
[7] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[8] Timothée Masquelier,et al. Acquisition of visual features through probabilistic spike-timing-dependent plasticity , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[9] Wolfgang Maass,et al. Bayesian Computation Emerges in Generic Cortical Microcircuits through Spike-Timing-Dependent Plasticity , 2013, PLoS Comput. Biol..
[10] Nikil D. Dutt,et al. Categorization and decision-making in a neurobiologically plausible spiking network using a STDP-like learning rule , 2013, Neural Networks.
[11] Konrad Paul Kording,et al. Bayesian integration in sensorimotor learning , 2004, Nature.
[12] Jochen Triesch,et al. Independent Component Analysis in Spiking Neurons , 2010, PLoS Comput. Biol..
[13] Wolfgang Maass,et al. Neural Dynamics as Sampling: A Model for Stochastic Computation in Recurrent Networks of Spiking Neurons , 2011, PLoS Comput. Biol..
[14] Timothée Masquelier,et al. Learning to recognize objects using waves of spikes and Spike Timing-Dependent Plasticity , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).
[15] Deepak Khosla,et al. Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition , 2014, International Journal of Computer Vision.
[16] Anthony S. Maida,et al. Multi-layer unsupervised learning in a spiking convolutional neural network , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).
[17] Timothée Masquelier,et al. Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..
[18] Anthony S. Maida,et al. A spiking network that learns to extract spike signatures from speech signals , 2016, Neurocomputing.
[19] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[20] David J. Field,et al. Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.
[21] Timothée Masquelier,et al. STDP-based spiking deep neural networks for object recognition , 2016, Neural Networks.
[22] Simon J. Thorpe,et al. Sparse spike coding in an asynchronous feed-forward multi-layer neural network using matching pursuit , 2004, Neurocomputing.
[23] Tobi Delbrück,et al. Training Deep Spiking Neural Networks Using Backpropagation , 2016, Front. Neurosci..
[24] Terrence J. Sejnowski,et al. The “independent components” of natural scenes are edge filters , 1997, Vision Research.
[25] Gert Cauwenberghs,et al. Event-driven contrastive divergence for spiking neuromorphic systems , 2013, Front. Neurosci..
[26] Kaushik Roy,et al. Unsupervised regenerative learning of hierarchical features in Spiking Deep Networks for object recognition , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[27] Sinan Kalkan,et al. Deep Hierarchies in the Primate Visual Cortex: What Can We Learn for Computer Vision? , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[28] Rajesh P. N. Rao,et al. Bayesian brain : probabilistic approaches to neural coding , 2006 .
[29] David Kappel,et al. STDP Installs in Winner-Take-All Circuits an Online Approximation to Hidden Markov Model Learning , 2014, PLoS Comput. Biol..
[30] Peter Dayan,et al. Probabilistic Computation in Spiking Populations , 2004, NIPS.
[31] Ammar Belatreche,et al. Bio-inspired hierarchical framework for multi-view face detection and pose estimation , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[32] Yee Whye Teh,et al. A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.
[33] Timothée Masquelier,et al. Bio-inspired unsupervised learning of visual features leads to robust invariant object recognition , 2015, Neurocomputing.
[34] P. Földiák,et al. Forming sparse representations by local anti-Hebbian learning , 1990, Biological Cybernetics.
[35] Wulfram Gerstner,et al. Predicting spike timing of neocortical pyramidal neurons by simple threshold models , 2006, Journal of Computational Neuroscience.
[36] Taeho Jo,et al. Improving Protein Fold Recognition by Deep Learning Networks , 2015, Scientific Reports.
[37] Katsuhiko Mori,et al. Convolutional spiking neural network model for robust face detection , 2002, Proceedings of the 9th International Conference on Neural Information Processing, 2002. ICONIP '02..
[38] Y. Dan,et al. Spike timing-dependent plasticity: from synapse to perception. , 2006, Physiological reviews.
[39] Harold Pashler,et al. Optimal Predictions in Everyday Cognition: The Wisdom of Individuals or Crowds? , 2008, Cogn. Sci..
[40] Wolfgang Maass,et al. Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity123 , 2016, eNeuro.
[41] Joel Zylberberg,et al. Inhibitory Interneurons Decorrelate Excitatory Cells to Drive Sparse Code Formation in a Spiking Model of V1 , 2013, The Journal of Neuroscience.
[42] Simei Gomes Wysoski,et al. Fast and adaptive network of spiking neurons for multi-view visual pattern recognition , 2008, Neurocomputing.
[43] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[44] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[45] Michael Robert DeWeese,et al. A Sparse Coding Model with Synaptically Local Plasticity and Spiking Neurons Can Account for the Diverse Shapes of V1 Simple Cell Receptive Fields , 2011, PLoS Comput. Biol..
[46] Tara N. Sainath,et al. Deep convolutional neural networks for LVCSR , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.
[47] Matthew Cook,et al. Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[48] Martin Rehn,et al. A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields , 2007, Journal of Computational Neuroscience.
[49] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[50] Yoshua. Bengio,et al. Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..
[51] Wulfram Gerstner,et al. Variational Learning for Recurrent Spiking Networks , 2011, NIPS.
[52] Rajesh P. N. Rao,et al. Probabilistic Models of the Brain: Perception and Neural Function , 2002 .
[53] Jean-Pascal Pfister,et al. Sequence learning with hidden units in spiking neural networks , 2011, NIPS.
[54] Yann LeCun,et al. Learning Invariant Feature Hierarchies , 2012, ECCV Workshops.