Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/114420
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Type: Journal article
Title: Generalized reconfigurable memristive dynamical system (MDS) for neuromorphic applications
Author: Bavandpour, M.
Soleimani, H.
Linares-Barranco, B.
Abbott, D.
Chua, L.
Citation: Frontiers in Neuroscience, 2015; 9(NOV):409
Publisher: Frontiers
Issue Date: 2015
ISSN: 1662-4548
1662-453X
Statement of
Responsibility: 
Mohammad Bavandpour, Hamid Soleimani, Bernabé Linares-Barranco, Derek Abbott and Leon O. Chua
Abstract: This study firstly presents (i) a novel general cellular mapping scheme for two dimensional neuromorphic dynamical systems such as bio-inspired neuron models, and (ii) an efficient mixed analog-digital circuit, which can be conveniently implemented on a hybrid memristor-crossbar/CMOS platform, for hardware implementation of the scheme. This approach employs 4n memristors and no switch for implementing an n-cell system in comparison with 2n (2) memristors and 2n switches of a Cellular Memristive Dynamical System (CMDS). Moreover, this approach allows for dynamical variables with both analog and one-hot digital values opening a wide range of choices for interconnections and networking schemes. Dynamical response analyses show that this circuit exhibits various responses based on the underlying bifurcation scenarios which determine the main characteristics of the neuromorphic dynamical systems. Due to high programmability of the circuit, it can be applied to a variety of learning systems, real-time applications, and analytically indescribable dynamical systems. We simulate the FitzHugh-Nagumo (FHN), Adaptive Exponential (AdEx) integrate and fire, and Izhikevich neuron models on our platform, and investigate the dynamical behaviors of these circuits as case studies. Moreover, error analysis shows that our approach is suitably accurate. We also develop a simple hardware prototype for experimental demonstration of our approach.
Keywords: Adaptive Exponential (AdEx) integrate and fire neuron model; FitzHugh-Nagumo (FHN) neuron model; Izhikevich neuron model; dynamical behavior analysis; general cellular mapping; hybrid memristor-crossbar/CMOS platform
Rights: © 2015 Bavandpour, Soleimani, Linares-Barranco, Abbott and Chua. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
RMID: 0030040892
DOI: 10.3389/fnins.2015.00409
Appears in Collections:Electrical and Electronic Engineering publications

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