Rahimi Azghadi, Mostafa, Al-Sarawi, Said, Iannella, Nicolangelo, and Abbott, Derek (2013) A new compact analog vlsi model for spike timing dependent plasticity. In: Proceedings of the 21st International Conference on Very Large Scale Integration (VLSI-SoC). pp. 7-12. From: 2013 IFIP/IEEE: 21st International Conference on Very Large Scale Integration (VLSI-SoC), 7-9 October 2013, Istanbul, Turkey.

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Abstract

Spike Timing Dependent Plasticity (STDP) is a time-based synaptic plasticity rule that has generated significant interest in the area of neuromorphic engineering and Very Large Scale Integration (VLSI) circuit design. During the last decade, STDP and STDP-like learning mechanisms have shown promising solutions for various real world applications, ranging from pattern recognition to robotics. This paper presents a novel analog VLSI model for STDP that possesses advantages compared to previously published VLSI STDP designs. The presented STDP circuit is capable of reproducing the outcomes of several well known experiments using various plasticity rules inducing STDP protocols that utilise pairs, triplets, and quadruplets of spike patterns. When the circuit is compared to state-of-the-art VLSI STDP circuits, it shows a compact and symmetric design that makes the proposed circuit a powerful component for use in designing STDP or time-based Hebbian learning experiments and applications.

Item ID:	45716
Item Type:	Conference Item (Research - E1)
ISBN:	978-1-4799-0524-9
Date Deposited:	06 Dec 2016 03:46
FoR Codes:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 33% 09 ENGINEERING > 0906 Electrical and Electronic Engineering > 090604 Microelectronics and Integrated Circuits @ 34% 10 TECHNOLOGY > 1007 Nanotechnology > 100705 Nanoelectronics @ 33%
SEO Codes:	97 EXPANDING KNOWLEDGE > 970108 Expanding Knowledge in the Information and Computing Sciences @ 33% 97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 33% 97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 34%
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