Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/84464
Citations
Scopus Web of ScienceĀ® Altmetric
?
?
Type: Journal article
Title: Tunable low energy, compact and high performance neuromorphic circuit for spike-based synaptic plasticity
Author: Azghadi, M.
Iannella, N.
Al-Sarawi, S.
Abbott, D.
Citation: PloS one, 2014; 9(2):e88326-1-e88326-14
Publisher: Public Library of Science
Issue Date: 2014
ISSN: 1932-6203
1932-6203
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.
Keywords: Hippocampus; Visual Cortex; Nerve Net; Neurons; Synapses; Humans; Synaptic Transmission; Action Potentials; Neuronal Plasticity; Neural Networks (Computer); Models, Neurological; Computer Simulation
RMID: 0020136403
DOI: 10.1371/journal.pone.0088326
Appears in Collections:Electrical and Electronic Engineering publications

Files in This Item:
File Description SizeFormat 
hdl_84464.pdfPublished version758.7 kBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.