Spiking neuromorphic networks with metal-oxide memristors

This is a brief review of our recent work on memristor-based spiking neuromorphic networks. We first describe the recent experimental demonstration of several most biology-plausible spike-time-dependent plasticity (STDP) windows in integrated metal-oxide memristors and, for the first time, the observed self-adaptive STDP, which may be crucial for spiking neural network applications. We then discuss recent theoretical work in which an analytical, data-verified STDP model was used to simulate operation of a spiking classifier of spatial-temporal patterns, and the capacity-to-fidelity tradeoff and noise immunity o f spiking spatial-temporal associative memories with local and global recording was evaluated.

[1]  Bernabé Linares-Barranco,et al.  On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex , 2011, Front. Neurosci..

[2]  K. D. Cantley,et al.  Spike-Timing-Dependent Plasticity Using Biologically Realistic Action Potentials and Low-Temperature Materials , 2013, IEEE Transactions on Nanotechnology.

[3]  Ligang Gao,et al.  High precision tuning of state for memristive devices by adaptable variation-tolerant algorithm , 2011, Nanotechnology.

[4]  Wofgang Maas,et al.  Networks of spiking neurons: the third generation of neural network models , 1997 .

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

[6]  Jennifer Hasler,et al.  Finding a roadmap to achieve large neuromorphic hardware systems , 2013, Front. Neurosci..

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

[8]  Y. Liu,et al.  Synaptic Learning and Memory Functions Achieved Using Oxygen Ion Migration/Diffusion in an Amorphous InGaZnO Memristor , 2012 .

[9]  Johannes Schemmel,et al.  Six Networks on a Universal Neuromorphic Computing Substrate , 2012, Front. Neurosci..

[10]  M. Prezioso,et al.  Memory Technologies for Neural Networks , 2015, 2015 IEEE International Memory Workshop (IMW).

[11]  Andrzej J. Kasinski,et al.  Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting , 2010, Neural Computation.

[12]  Sebastian A. Wills,et al.  Computation with Spiking Neurons , 2004 .

[13]  Farnood Merrikh-Bayat,et al.  Self-Adaptive Spike-Time-Dependent Plasticity of Metal-Oxide Memristors , 2015, Scientific Reports.

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

[15]  D. Johnston,et al.  Regulation of Synaptic Efficacy by Coincidence of Postsynaptic APs and EPSPs , 1997 .

[16]  S. Thorpe,et al.  Spike Timing Dependent Plasticity Finds the Start of Repeating Patterns in Continuous Spike Trains , 2008, PloS one.

[17]  Bipin Rajendran,et al.  Novel synaptic memory device for neuromorphic computing , 2014, Scientific Reports.

[18]  Konstantin K. Likharev,et al.  CrossNets: Neuromorphic Hybrid CMOS/Nanoelectronic Networks , 2011 .

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

[20]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[21]  Fabien Alibart,et al.  Plasticity in memristive devices for spiking neural networks , 2015, Front. Neurosci..

[22]  J Joshua Yang,et al.  Memristive devices for computing. , 2013, Nature nanotechnology.