NDLI: Nonlinear behavior of memristive devices during tuning process and its impact on STDP learning rule in memristive neural networks

Content Provider	Springer Nature Link
Author	Merrikh Bayat, Farod Shouraki, Saeed Bagheri
Copyright Year	2014
Abstract	It is now widely accepted that memristive devices are promising candidates for the emulation of the behavior of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive device can be tuned actively for example by the application of voltage or current. In addition, it is also possible to fabricate high density of memristive devices through the nano-crossbar structures. In this paper, we will show that there are some problems associated with memristive devices, which are playing the role of biological synapses. For example, we show that the variation rate of the memristance depends completely on the initial state of the device, and therefore, it can change significantly with time during the learning phase. This phenomenon can degrade the performance of learning methods like spike timing-dependent plasticity and cause the corresponding neuromorphic systems to become unstable. We also illustrate that using two serially connected memristive devices with different polarities as a synapse can somewhat fix the aforementioned problem.
Starting Page	67
Ending Page	75
Page Count	9
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	26
Issue Number	1
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2014-08-20
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Memristive device Synapse Hebbian learning Spike timing-dependent plasticity Neuromorphic systems Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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