Nanoscale memristor device as synapse in neuromorphic systems.

By S. Jo, Ting Chang, Idongesit E. Ebong, Bhavitavya Bhadviya, P. Mazumder, Wei Yang Lu. 2010

3,901 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Electrical and Electronic Engineering

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In Cellular and Molecular Neuroscience

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “The progress of EPANNs will crucially depend on implementations that take advantage of the increased computational power of parallel computation with GPUs and neuromorphic hardware (Jo et al., 2010; Monroe, 2014).”
Spin-torque building blocks. (2014, background): “Moreover, this simple electric control of memristors’ conductance allows the emulation of complex bioinspired learning rules such as spike-timing-dependent-plasticity (STDP) [97].”
Neural Synaptic Weighting With a Pulse-Based Memristor Circuit (2012, background): “recently reported the development of a new type of memristor which shows improved analog characteristics using Ag and Si in a sandwiched layer [25].”
Memristor-based memory: The sneak paths problem and solutions (2013, methodology): “In addition to be used as a memory element [2, 17–26], the memristor has found many applications in oscillators [27–30], logic and arithmetic circuits [31, 32], programmable analog circuits [33, 34], and in modeling and emulation of natural phenomena [35, 36].”
Pattern classification by memristive crossbar circuits using ex situ and in situ training (2013, background): “These milestones, in turn, revived interest in the development of memristor-based ANNs and led to numerous few-or single-memristor demonstrations of synaptic functionality and simple associative memory 21–28 , as well as the theoretical modelling of large-scale networks 10,29–33 .”
Artificial synapse network on inorganic proton conductor for neuromorphic systems (2013, background): “[5] Recently, resistive switching memory, memristors or atomic switch have been investigated in biologically inspired neuromorphic circuits.[6-11] Important synaptic learning rules such as spike-timing dependent plasticity (STDP) and short-term memory to long-term memory transition have been demonst”
Evolution of Plastic Learning in Spiking Networks via Memristive Connections (2012, methodology): “Silver silicide memristors have been shown to function in neural architectures [32].”