Generalized reconfigurable memristive dynamical system (MDS) for neuromorphic applications

Mohammad Bavandpour; Hamid Soleimani; Bernabé Linares-Barranco; Derek Abbott; Leon O Chua

doi:10.3389/fnins.2015.00409

Generalized reconfigurable memristive dynamical system (MDS) for neuromorphic applications

Front Neurosci. 2015 Nov 3:9:409. doi: 10.3389/fnins.2015.00409. eCollection 2015.

Authors

Mohammad Bavandpour¹, Hamid Soleimani², Bernabé Linares-Barranco³, Derek Abbott⁴, Leon O Chua⁵

Affiliations

¹ Department of Electrical and Computer Engineering, University of California, Santa Barbara Santa Barbara, CA, USA.
² Department of Bioengineering of Imperial College London, UK.
³ Instituto de Microelectrónica de Sevilla, IMSE-CNM, Universidad de Sevilla and CSIC Sevilla, Spain.
⁴ School of Electrical and Electronic Engineering, The University of Adelaide Adelaide, SA, Australia.
⁵ Department of Electrical and Computer Engineering, University of California, Berkeley Berkeley, CA, USA.

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.