An approximate backpropagation learning rule for memristor based neural networks using synaptic plasticity
暂无分享,去创建一个
Witali L. Dunin-Barkowski | Iakov M. Karandashev | Dmitrii Negrov | V. V. Shakirov | Yu. Matveyev | Andrei Zenkevich | I. Karandashev | W. Dunin-Barkowski | D. Negrov | Yury Matveyev | V. Shakirov | Andrei Zenkevich
[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] Leon O. Chua,et al. Memristor Bridge Synapses , 2012, Proceedings of the IEEE.
[3] Jacques-Olivier Klein,et al. Robust neural logic block (NLB) based on memristor crossbar array , 2011, 2011 IEEE/ACM International Symposium on Nanoscale Architectures.
[4] Daniel Cownden,et al. Random feedback weights support learning in deep neural networks , 2014, ArXiv.
[5] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[6] Wei Yang Lu,et al. Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.
[7] Leon O. Chua,et al. Everything You Wish to Know About Memristors but Are Afraid to Ask , 2015, Handbook of Memristor Networks.
[8] Huamin Wang,et al. Pavlov associative memory in a memristive neural network and its circuit implementation , 2016, Neurocomputing.
[9] Tarek M. Taha,et al. Enabling back propagation training of memristor crossbar neuromorphic processors , 2014, 2014 International Joint Conference on Neural Networks (IJCNN).
[10] Luca Maria Gambardella,et al. Better Digit Recognition with a Committee of Simple Neural Nets , 2011, 2011 International Conference on Document Analysis and Recognition.
[11] O. Richard,et al. 10×10nm2 Hf/HfOx crossbar resistive RAM with excellent performance, reliability and low-energy operation , 2011, 2011 International Electron Devices Meeting.
[12] Giacomo Indiveri,et al. Integration of nanoscale memristor synapses in neuromorphic computing architectures , 2013, Nanotechnology.
[14] Bernabé Linares-Barranco,et al. On neuromorphic spiking architectures for asynchronous STDP memristive systems , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[15] Mika Laiho,et al. Cellular nanoscale network cell with memristors for local implication logic and synapses , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[16] Garrett S. Rose,et al. Memristor-Based Neural Logic Blocks for Nonlinearly Separable Functions , 2013, IEEE Transactions on Computers.
[17] Bernard Widrow,et al. The No-Prop algorithm: A new learning algorithm for multilayer neural networks , 2013, Neural Networks.
[18] Gert Cauwenberghs,et al. Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.
[19] A. Ayatollahi,et al. Implementation of biologically plausible spiking neural network models on the memristor crossbar-based CMOS/nano circuits , 2009, 2009 European Conference on Circuit Theory and Design.
[20] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[21] Fabien Alibart,et al. Pattern classification by memristive crossbar circuits using ex situ and in situ training , 2013, Nature Communications.
[22] Piotr Dudek,et al. Gradient-descent-based learning in memristive crossbar arrays , 2015, 2015 International Joint Conference on Neural Networks (IJCNN).
[23] Shukai Duan,et al. Multilayer RTD-memristor-based cellular neural networks for color image processing , 2015, Neurocomputing.
[24] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[25] Leon O. Chua,et al. A Circuit-Based Learning Architecture for Multilayer Neural Networks With Memristor Bridge Synapses , 2015, IEEE Transactions on Circuits and Systems I: Regular Papers.
[26] Jawar Singh,et al. Linearly separable pattern classification using memristive crossbar circuits , 2014, Fifteenth International Symposium on Quality Electronic Design.
[27] Cory Merkel,et al. Heterogeneous CMOS/memristor hardware neural networks for real-time target classification , 2014, Sensing Technologies + Applications.
[28] Konstantin K. Likharev,et al. CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks , 2011 .
[29] P. Werbos,et al. Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .
[30] Damien Querlioz,et al. Simulation of a memristor-based spiking neural network immune to device variations , 2011, The 2011 International Joint Conference on Neural Networks.
[31] A. Zenkevich,et al. Multilevel resistive switching in ternary HfxAl1-xOy oxide with graded Al depth profile , 2013 .
[32] Jason Cong,et al. Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks , 2015, FPGA.
[33] Martin A. Riedmiller,et al. A direct adaptive method for faster backpropagation learning: the RPROP algorithm , 1993, IEEE International Conference on Neural Networks.
[34] A. Zenkevich,et al. Resistive switching and synaptic properties of fully atomic layer deposition grown TiN/HfO2/TiN devices , 2015 .
[35] Dhireesha Kudithipudi,et al. Comparison of Off-Chip Training Methods for Neuromemristive Systems , 2015, 2015 28th International Conference on VLSI Design.
[36] Avinoam Kolodny,et al. Memristor-Based Multilayer Neural Networks With Online Gradient Descent Training , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[37] Byoungil Lee,et al. Nanoelectronic programmable synapses based on phase change materials for brain-inspired computing. , 2012, Nano letters.
[38] Pinaki Mazumder,et al. CMOS and Memristor-Based Neural Network Design for Position Detection , 2012, Proceedings of the IEEE.
[39] Leon O. Chua,et al. Neural Synaptic Weighting With a Pulse-Based Memristor Circuit , 2012, IEEE Transactions on Circuits and Systems I: Regular Papers.