Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity

Rahimi Azghadi, Mostafa, Iannella, Nicolangelo, Al-Sarawi, Said, and Abbott, Derek (2014) Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity. PLoS ONE, 9 (2). e88326. pp. 1-14.

[img]
Preview
PDF (Published Version) - Published Version
Available under License Creative Commons Attribution.

Download (684kB) | Preview
View at Publisher Website: http://dx.doi.org/10.1371/journal.pone.0...
 
14
55


Abstract

Cortical circuits in the brain have long been recognised for their information processing capabilities and have been studied both experimentally and theoretically via spiking neural networks. Neuromorphic engineers are primarily concerned with translating the computational capabilities of biological cortical circuits, using the Spiking Neural Network (SNN) paradigm, into in silico applications that can mimic the behaviour and capabilities of real biological circuits/systems. These capabilities include low power consumption, compactness, and relevant dynamics. In this paper, we propose a new accelerated-time circuit that has several advantages over its previous neuromorphic counterparts in terms of compactness, power consumption, and capability to mimic the outcomes of biological experiments. The presented circuit simulation results demonstrate that, in comparing the new circuit to previous published synaptic plasticity circuits, reduced silicon area and lower energy consumption for processing each spike is achieved. In addition, it can be tuned in order to closely mimic the outcomes of various spike timing- and rate-based synaptic plasticity experiments. The proposed circuit is also investigated and compared to other designs in terms of tolerance to mismatch and process variation. Monte Carlo simulation results show that the proposed design is much more stable than its previous counterparts in terms of vulnerability to transistor mismatch, which is a significant challenge in analog neuromorphic design. All these features make the proposed design an ideal circuit for use in large scale SNNs, which aim at implementing neuromorphic systems with an inherent capability that can adapt to a continuously changing environment, thus leading to systems with significant learning and computational abilities.

Item ID: 45717
Item Type: Article (Research - C1)
ISSN: 1932-6203
Additional Information:

© 2014 Rahimi Azghadi et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Date Deposited: 06 Dec 2016 03:20
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 34%
10 TECHNOLOGY > 1007 Nanotechnology > 100705 Nanoelectronics @ 33%
09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 33%
SEO Codes: 97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 34%
97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 33%
97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 33%
Downloads: Total: 55
Last 12 Months: 23
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page