DocumentCode
28020
Title
Memristive Hebbian Plasticity Model: Device Requirements for the Emulation of Hebbian Plasticity Based on Memristive Devices
Author
Ziegler, Martin ; Riggert, Christoph ; Hansen, Mirko ; Bartsch, Thorsten ; Kohlstedt, Hermann
Author_Institution
AG Nanoelektronik, Christian-Albrechts-Univ. zu Kiel, Kiel, Germany
Volume
9
Issue
2
fYear
2015
fDate
Apr-15
Firstpage
197
Lastpage
206
Abstract
In this work we present a phenomenological model for synaptic plasticity suitable to describe common plasticity measurements of memristive devices. We show evidence that the presented model is basically compatible with advanced biophysical plasticity models, which account for a large body of experimental data on spike-timing-depending plasticity (STDP) as an asymmetric form of Hebbian learning. The basic characteristics of our model are a saturation of the synaptic weight growth and a weight dependent learning rate. Moreover, it accounts for common resistive switching behaviors of memristive devices under voltage pulse application and allows to study essential requirements of individual memristive devices for the emulation of Hebbian plasticity in neuromorphic circuits. In this respect, memristive devices based on mixed ionic/electronic and one exclusively electronic mechanism are explored. The ionic/electronic devices consist of the layer sequence metal/isolator/metal and represent today´s most popular devices. The electronic device is a MemFlash-cell which is based on a conventional floating gate transistor in a diode configuration wiring scheme exhibiting a memristive (pinched) I-V characteristic.
Keywords
bioelectric potentials; neurophysiology; physiological models; MemFlash-cell; diode configuration wiring scheme; floating gate transistor; hebbian plasticity emulation; memristive Hebbian plasticity model; metal-isolator-metal layer sequence; mixed ionic-electronic devices; neuromorphic circuits; phenomenological model; spike-timing-depending plasticity; synaptic plasticity; voltage pulse application; weight dependent learning rate; Biological system modeling; Current measurement; Integrated circuit modeling; Neurons; Resistance; Tin; Voltage measurement; Floating gate transistors; Hebbian learning; memristive devices; neuromorphic engineering; synaptic plasticity;
fLanguage
English
Journal_Title
Biomedical Circuits and Systems, IEEE Transactions on
Publisher
ieee
ISSN
1932-4545
Type
jour
DOI
10.1109/TBCAS.2015.2410811
Filename
7086091
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