Learning of spatiotemporal patterns in a spiking neural network with resistive switching synapses
暂无分享,去创建一个
Alessandro Calderoni | Daniele Ielmini | Valerio Milo | Giacomo Pedretti | Roberto Carboni | Nirmal Ramaswamy | Wei Wang | Alessandro S Spinelli | D. Ielmini | Wei Wang | A. Spinelli | V. Milo | G. Pedretti | A. Calderoni | N. Ramaswamy | R. Carboni
[1] Ryad Benosman,et al. A spiking neural network model of 3D perception for event-based neuromorphic stereo vision systems , 2017, Scientific Reports.
[2] Pritish Narayanan,et al. Equivalent-accuracy accelerated neural-network training using analogue memory , 2018, Nature.
[3] Simone Balatti,et al. A 2-transistor/1-resistor artificial synapse capable of communication and stochastic learning in neuromorphic systems , 2015, Front. Neurosci..
[4] Malu Zhang,et al. Efficient training of supervised spiking neural networks via the normalized perceptron based learning rule , 2017, Neurocomputing.
[5] Rémi Monasson,et al. Theory of spike timing-based neural classifiers. , 2010, Physical review letters.
[6] S. Ambrogio,et al. Analytical Modeling of Oxide-Based Bipolar Resistive Memories and Complementary Resistive Switches , 2014, IEEE Transactions on Electron Devices.
[7] E. Eleftheriou,et al. All-memristive neuromorphic computing with level-tuned neurons , 2016, Nanotechnology.
[8] Fred J. Damerau,et al. A technique for computer detection and correction of spelling errors , 1964, CACM.
[9] M. Häusser. The Hodgkin-Huxley theory of the action potential , 2000, Nature Neuroscience.
[10] S. Thorpe,et al. Spike times make sense , 2005, Trends in Neurosciences.
[11] Sergey L. Gratiy,et al. Fully integrated silicon probes for high-density recording of neural activity , 2017, Nature.
[12] L. F. Abbott,et al. Building functional networks of spiking model neurons , 2016, Nature Neuroscience.
[13] Charles Kemp,et al. How to Grow a Mind: Statistics, Structure, and Abstraction , 2011, Science.
[14] Fabien Alibart,et al. Pattern classification by memristive crossbar circuits using ex situ and in situ training , 2013, Nature Communications.
[15] A. Calderoni,et al. Performance comparison of O-based and Cu-based ReRAM for high-density applications , 2014, 2014 IEEE 6th International Memory Workshop (IMW).
[16] Robert Gütig,et al. To spike, or when to spike? , 2014, Current Opinion in Neurobiology.
[17] Peng Lin,et al. Fully memristive neural networks for pattern classification with unsupervised learning , 2018 .
[18] D. Ielmini,et al. Demonstration of hybrid CMOS/RRAM neural networks with spike time/rate-dependent plasticity , 2016, 2016 IEEE International Electron Devices Meeting (IEDM).
[19] Sidney B. Williams,et al. Differential modulation of repetitive firing and synchronous network activity in neocortical interneurons by inhibition of A-type K+ channels and Ih , 2015, Front. Cell. Neurosci..
[20] Alessandro Calderoni,et al. Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM , 2016, IEEE Transactions on Electron Devices.
[21] Walter Senn,et al. Learning Real-World Stimuli in a Neural Network with Spike-Driven Synaptic Dynamics , 2007, Neural Computation.
[22] Wolfgang Maass,et al. Networks of Spiking Neurons: The Third Generation of Neural Network Models , 1996, Electron. Colloquium Comput. Complex..
[23] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[24] Malu Zhang,et al. Supervised learning in spiking neural networks with noise-threshold , 2017, Neurocomputing.
[25] Thomas P. Parnell,et al. Temporal correlation detection using computational phase-change memory , 2017, Nature Communications.
[26] Stefano Panzeri,et al. Distinct timescales of population coding across cortex , 2017, Nature.
[27] Huaqiang Wu,et al. An artificial nociceptor based on a diffusive memristor , 2018, Nature Communications.
[28] G. Silberberg,et al. Measurement and analysis of postsynaptic potentials using a novel voltage-deconvolution method. , 2008, Journal of neurophysiology.
[29] Paolo Fantini,et al. Unsupervised Learning by Spike Timing Dependent Plasticity in Phase Change Memory (PCM) Synapses , 2016, Front. Neurosci..
[30] W. Pitts,et al. A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.
[31] J Joshua Yang,et al. Memristive devices for computing. , 2013, Nature nanotechnology.
[32] Brice Bathellier,et al. Temporal asymmetries in auditory coding and perception reflect multi-layered nonlinearities , 2016, Nature Communications.
[33] Andrzej J. Kasinski,et al. Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting , 2010, Neural Computation.
[34] B. Delgutte,et al. A Physiologically Based Model of Interaural Time Difference Discrimination , 2004, The Journal of Neuroscience.
[35] Alexander Borst,et al. Information theory and neural coding , 1999, Nature Neuroscience.
[36] Wofgang Maas,et al. Networks of spiking neurons: the third generation of neural network models , 1997 .
[37] Jiaming Zhang,et al. Analogue signal and image processing with large memristor crossbars , 2017, Nature Electronics.
[38] Gregory V. Bard,et al. Spelling-Error Tolerant, Order-Independent Pass-Phrases via the Damerau-Levenshtein String-Edit Distance Metric , 2007, ACSW.
[39] Huajin Tang,et al. Precise-Spike-Driven Synaptic Plasticity: Learning Hetero-Association of Spatiotemporal Spike Patterns , 2013, PloS one.
[40] Claudia Clopath,et al. Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level , 2017, Nature Communications.
[41] Denis Fize,et al. Speed of processing in the human visual system , 1996, Nature.
[42] Heiner Giefers,et al. Mixed-precision in-memory computing , 2017, Nature Electronics.
[43] Daniele Ielmini,et al. Resistive switching memories based on metal oxides: mechanisms, reliability and scaling , 2016 .
[44] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[45] Steven M. Peterson,et al. A spatiotemporal coding mechanism for background-invariant odor recognition , 2013, Nature Neuroscience.
[46] Farnood Merrikh-Bayat,et al. Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.
[47] A S Spinelli,et al. Memristive neural network for on-line learning and tracking with brain-inspired spike timing dependent plasticity , 2017, Scientific Reports.
[48] Catherine E. Graves,et al. Memristor‐Based Analog Computation and Neural Network Classification with a Dot Product Engine , 2018, Advanced materials.
[49] Wei D. Lu,et al. Sparse coding with memristor networks. , 2017, Nature nanotechnology.
[50] J. Yang,et al. Memristors with diffusive dynamics as synaptic emulators for neuromorphic computing. , 2017, Nature materials.
[51] John F. Roddick,et al. Proceedings of the fifth Australasian symposium on ACSW frontiers - Volume 68 , 2007 .
[52] E. Vianello,et al. HfO2-Based OxRAM Devices as Synapses for Convolutional Neural Networks , 2015, IEEE Transactions on Electron Devices.