暂无分享,去创建一个
[1] 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).
[2] Wulfram Gerstner,et al. SPIKING NEURON MODELS Single Neurons , Populations , Plasticity , 2002 .
[3] Sepp Hochreiter,et al. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[4] Sophie Denève,et al. Spike-Based Population Coding and Working Memory , 2011, PLoS Comput. Biol..
[5] Terrence J. Sejnowski,et al. Analysis of hidden units in a layered network trained to classify sonar targets , 1988, Neural Networks.
[6] Deepak Khosla,et al. Spiking Deep Convolutional Neural Networks for Energy-Efficient Object Recognition , 2014, International Journal of Computer Vision.
[7] Iulia-Alexandra Lungu,et al. Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks , 2016, ArXiv.
[8] Jim D. Garside,et al. Overview of the SpiNNaker System Architecture , 2013, IEEE Transactions on Computers.
[9] Christian K. Machens,et al. Efficient codes and balanced networks , 2016, Nature Neuroscience.
[10] J. Serences,et al. Spatial attention improves the quality of population codes in human visual cortex. , 2010, Journal of neurophysiology.
[11] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[12] Young C. Yoon,et al. LIF and Simplified SRM Neurons Encode Signals Into Spikes via a Form of Asynchronous Pulse Sigma–Delta Modulation , 2017, IEEE Transactions on Neural Networks and Learning Systems.
[13] Chris Eliasmith,et al. Training Spiking Deep Networks for Neuromorphic Hardware , 2016, ArXiv.
[14] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[15] Nitish Srivastava,et al. Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..
[16] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[17] L. Abbott,et al. Synaptic computation , 2004, Nature.
[18] S. Laughlin,et al. An Energy Budget for Signaling in the Grey Matter of the Brain , 2001, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.
[19] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Karl J. Friston. The free-energy principle: a unified brain theory? , 2010, Nature Reviews Neuroscience.
[21] Adrienne L. Fairhall,et al. Efficiency and ambiguity in an adaptive neural code , 2001, Nature.
[22] Andrew S. Cassidy,et al. Convolutional networks for fast, energy-efficient neuromorphic computing , 2016, Proceedings of the National Academy of Sciences.
[23] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[24] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[25] W. Gerstner,et al. Temporal whitening by power-law adaptation in neocortical neurons , 2013, Nature Neuroscience.
[26] Sergey Ioffe,et al. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.
[27] Sander M. Bohte,et al. Efficient Spike-Coding with Multiplicative Adaptation in a Spike Response Model , 2012, NIPS.
[28] Sir G. Archaeopteryx. Object-based attention in the primary visual cortex of the macaque monkey , 1998 .