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Biorealistic Implementation of Synaptic Functions with Oxide Memristors through Internal Ionic Dynamics
Author(s) -
Du Chao,
Ma Wen,
Chang Ting,
Sheridan Patrick,
Lu Wei D.
Publication year - 2015
Publication title -
advanced functional materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.069
H-Index - 322
eISSN - 1616-3028
pISSN - 1616-301X
DOI - 10.1002/adfm.201501427
Subject(s) - memristor , neuromorphic engineering , computer science , focus (optics) , set (abstract data type) , simple (philosophy) , materials science , artificial intelligence , topology (electrical circuits) , artificial neural network , electronic engineering , electrical engineering , physics , engineering , optics , philosophy , epistemology , programming language
Memristors have attracted broad interest as a promising candidate for future memory and computing applications. Particularly, it is believed that memristors can effectively implement synaptic functions and enable efficient neuromorphic systems. Most previous studies, however, focus on implementing specific synaptic learning rules by carefully engineering external programming parameters instead of focusing on emulating the internal cause that leads to the apparent learning rules. Here, it is shown that by taking advantage of the different time scales of internal oxygen vacancy ( V O ) dynamics in an oxide‐based memristor, diverse synaptic functions at different time scales can be implemented naturally. Mathematically, the device can be effectively modeled as a second‐order memristor with a simple set of equations including multiple state variables. Not only is this approach more biorealistic and easier to implement, by focusing on the fundamental driving mechanisms it allows the development of complete theoretical and experimental frameworks for biologically inspired computing systems.