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Memristive Synapses and Neurons for Bioinspired Computing
Author(s) -
Yang Rui,
Huang HeMing,
Guo Xin
Publication year - 2019
Publication title -
advanced electronic materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.25
H-Index - 56
ISSN - 2199-160X
DOI - 10.1002/aelm.201900287
Subject(s) - neuromorphic engineering , neuroscience , memristor , computer science , synaptic plasticity , reservoir computing , synapse , artificial intelligence , nanotechnology , artificial neural network , biology , materials science , engineering , electronic engineering , recurrent neural network , biochemistry , receptor
To realize highly efficient neuromorphic computing that is comparable to biological counterparts, bioinspired computing systems, consisting of biorealistic artificial synapses and neurons, are developed with memristive devices with native dynamics resembling biological synapses and neurons. Tremendous materials and devices have been successfully used to emulate diverse functions of synapses, as well as neurons, in the last decade. Herein, approaches to realize certain synaptic or neuronal functions are introduced with state‐of‐art experimental demonstrations. First, the dynamics and working principles of biological synapses and neurons are briefly presented to provide guidance for developing biorealistic synapses and neurons. Second, recent advances in the development of memristive synapses with homosynaptic and heterosynaptic plasticity are disscussed. In particular, approaches to realize the important learning rules, like spiking‐timing‐dependent plasticity and Bienenstock–Cooper–Munro learning rules, are elaborated according to the level of faithfulness to biological synapses. Memristive neurons, including bioplausible neurons and biophysical neurons, are described. Finally, challenges and perspectives for bioinspired computing based on memristive devices are briefly discussed.