Integrated analog neurons inspired by mimicking synapses with metal-oxide memristive devices

[1]  Jun Deguchi,et al.  A Neuromorphic Chip Optimized for Deep Learning and CMOS Technology With Time-Domain Analog and Digital Mixed-Signal Processing , 2017, IEEE Journal of Solid-State Circuits.

[2]  Takao Marukame,et al.  Nonlinear Operation of Static-Binary Neuron Circuits and Dynamic Memristive Devices for STDP Learning , 2019, 2019 IEEE International Symposium on Circuits and Systems (ISCAS).

[3]  Giacomo Indiveri,et al.  A VLSI array of low-power spiking neurons and bistable synapses with spike-timing dependent plasticity , 2006, IEEE Transactions on Neural Networks.

[4]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[5]  Andrew S. Cassidy,et al.  A million spiking-neuron integrated circuit with a scalable communication network and interface , 2014, Science.

[6]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[7]  Takayuki Ishikawa,et al.  Giant oscillations in spin-dependent tunneling resistances as a function of barrier thickness in fully epitaxial magnetic tunnel junctions with a MgO barrier , 2010 .

[8]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[9]  Gert Cauwenberghs,et al.  Neuromorphic Silicon Neuron Circuits , 2011, Front. Neurosci.

[10]  Takao Marukame,et al.  Proposal, analysis and demonstration of Analog/Digital-mixed Neural Networks based on memristive device arrays , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[11]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[12]  Tetsuya Asai,et al.  Error Tolerance Analysis of Deep Learning Hardware Using a Restricted Boltzmann Machine Toward Low-Power Memory Implementation , 2017, IEEE Transactions on Circuits and Systems II: Express Briefs.

[13]  Giacomo Indiveri,et al.  A reconfigurable on-line learning spiking neuromorphic processor comprising 256 neurons and 128K synapses , 2015, Front. Neurosci..

[14]  Matthew Cook,et al.  Unsupervised learning of digit recognition using spike-timing-dependent plasticity , 2015, Front. Comput. Neurosci..

[15]  T. Serrano-Gotarredona,et al.  STDP and STDP variations with memristors for spiking neuromorphic learning systems , 2013, Front. Neurosci..

[16]  E. Vianello,et al.  Bio-Inspired Stochastic Computing Using Binary CBRAM Synapses , 2013, IEEE Transactions on Electron Devices.

[17]  Farnood Merrikh-Bayat,et al.  Training and operation of an integrated neuromorphic network based on metal-oxide memristors , 2014, Nature.

[18]  H.-S. Philip Wong,et al.  In-memory computing with resistive switching devices , 2018, Nature Electronics.

[19]  Tetsuya Asai,et al.  FPGA Implementation of a Scalable and Highly Parallel Architecture for Restricted Boltzmann Machines , 2016 .

[20]  Takao Marukame,et al.  In-memory Reinforcement Learning with Moderately-Stochastic Conductance Switching of Ferroelectric Tunnel Junctions , 2019, 2019 Symposium on VLSI Technology.

[21]  Yoshifumi Nishi,et al.  Artificial neuron operations and spike-timing-dependent plasticity using memristive devices for brain-inspired computing , 2018 .

[22]  Tetsuya Asai,et al.  Robustness of hardware-oriented restricted Boltzmann machines in deep belief networks for reliable processing , 2016 .

[23]  Wei Yang Lu,et al.  Nanoscale memristor device as synapse in neuromorphic systems. , 2010, Nano letters.

[24]  Jiantao Zhou,et al.  Stochastic Memristive Devices for Computing and Neuromorphic Applications , 2013, Nanoscale.

[25]  Giacomo Indiveri,et al.  Event-based circuits for controlling stochastic learning with memristive devices in neuromorphic architectures , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[26]  A. Thomas,et al.  The Memristive Magnetic Tunnel Junction as a Nanoscopic Synapse‐Neuron System , 2012, Advanced materials.

[27]  Takayuki Ishikawa,et al.  Highly spin-polarized tunneling in fully epitaxial Co2Cr0.6Fe0.4Al∕MgO∕Co50Fe50 magnetic tunnel junctions with exchange biasing , 2007 .

[28]  Wulfram Gerstner,et al.  Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns , 1993, Biological Cybernetics.

[29]  Wei Lu,et al.  Short-term Memory to Long-term Memory Transition in a Nanoscale Memristor , 2022 .

[30]  Colin J. Akerman,et al.  Random synaptic feedback weights support error backpropagation for deep learning , 2016, Nature Communications.

[31]  G. Bi,et al.  Synaptic Modifications in Cultured Hippocampal Neurons: Dependence on Spike Timing, Synaptic Strength, and Postsynaptic Cell Type , 1998, The Journal of Neuroscience.