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
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;
Journal_Title :
Biomedical Circuits and Systems, IEEE Transactions on
DOI :
10.1109/TBCAS.2015.2410811