Robust Sequence Recognition Using Biologically Inspired Temporal Learning Mechanisms
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[1] Ming Zhang,et al. Combining Bottom-Up and Top-Down Visual Mechanisms for Color Constancy Under Varying Illumination , 2019, IEEE Transactions on Image Processing.
[2] Sarah M. N. Woolley,et al. Sparse and Background-Invariant Coding of Vocalizations in Auditory Scenes , 2013, Neuron.
[3] Hojjat Adeli,et al. Spiking Neural Networks , 2009, Int. J. Neural Syst..
[4] A. Bruckstein,et al. K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .
[5] Haizhou Li,et al. A Spiking Neural Network System for Robust Sequence Recognition , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[6] M. Elad,et al. $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.
[7] Shao-Bing Gao,et al. A Retinal Mechanism Inspired Color Constancy Model , 2016, IEEE Transactions on Image Processing.
[8] Daniel Jachyra,et al. Neural Network Structure for Spatio-Temporal Long-Term Memory , 2012, IEEE Transactions on Neural Networks and Learning Systems.
[9] J. C. Middlebrooks,et al. Cortical representation of auditory space: information-bearing features of spike patterns. , 2002, Journal of neurophysiology.
[10] Yong Zhang,et al. A Digital Liquid State Machine With Biologically Inspired Learning and Its Application to Speech Recognition , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[11] J. Simon,et al. Neural coding of continuous speech in auditory cortex during monaural and dichotic listening. , 2012, Journal of neurophysiology.
[12] Haibo He,et al. Spatio–Temporal Memories for Machine Learning: A Long-Term Memory Organization , 2009, IEEE Transactions on Neural Networks.
[13] Yong-Jie Li,et al. Underwater Image Enhancement Using Adaptive Retinal Mechanisms , 2019, IEEE Transactions on Image Processing.
[14] Haizhou Li,et al. A Biologically Plausible Speech Recognition Framework Based on Spiking Neural Networks , 2018, 2018 International Joint Conference on Neural Networks (IJCNN).
[15] H. Sompolinsky,et al. The tempotron: a neuron that learns spike timing–based decisions , 2006, Nature Neuroscience.
[16] D. Jin. Decoding spatiotemporal spike sequences via the finite state automata dynamics of spiking neural networks , 2008 .
[17] Yongjie Li,et al. A Color Constancy Model with Double-Opponency Mechanisms , 2013, 2013 IEEE International Conference on Computer Vision.
[18] Andrew S. Cassidy,et al. A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.
[19] J J Hopfield,et al. What is a moment? Transient synchrony as a collective mechanism for spatiotemporal integration. , 2001, Proceedings of the National Academy of Sciences of the United States of America.
[20] A. Bruckstein,et al. On the uniqueness of overcomplete dictionaries, and a practical way to retrieve them , 2006 .
[21] Shih-Chii Liu,et al. Speaker-independent isolated digit recognition using an AER silicon cochlea , 2011, 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS).
[22] Kai-Fu Yang,et al. Color Constancy Using Double-Opponency , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[23] Wofgang Maas,et al. Networks of spiking neurons: the third generation of neural network models , 1997 .
[24] Rong Xiao,et al. A neurally inspired pattern recognition approach with latency-phase encoding and precise-spike-driven rule in spiking neural network , 2017, 2017 IEEE International Conference on Cybernetics and Intelligent Systems (CIS) and IEEE Conference on Robotics, Automation and Mechatronics (RAM).
[25] Anthony S. Maida,et al. A spiking network that learns to extract spike signatures from speech signals , 2016, Neurocomputing.
[26] Dezhe Z Jin,et al. Spiking neural network for recognizing spatiotemporal sequences of spikes. , 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.
[27] Hao,et al. Improving color constancy by selecting suitable set of training images , 2019 .
[28] H. Sompolinsky,et al. Time-Warp–Invariant Neuronal Processing , 2009, PLoS biology.
[29] Shao-Bing Gao,et al. A Retina Inspired Model for Enhancing Visibility of Hazy Images , 2015, Front. Comput. Neurosci..