Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges

Rahimi Azghadi, Mostafa, Iannella, Nicolangelo, Al-Sarawi, Said F., Indiveri, Giacomo, and Abbott, Derek (2014) Spike-based synaptic plasticity in silicon: design, implementation, application, and challenges. Proceedings of the IEEE, 102 (5). pp. 717-737.

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The ability to carry out signal processing, classification, recognition, and computation in artificial spiking neural networks (SNNs) is mediated by their synapses. In particular, through activity-dependent alteration of their efficacies, synapses play a fundamental role in learning. The mathematical prescriptions under which synapses modify their weights are termed synaptic plasticity rules. These learning rules can be based on abstract computational neuroscience models or on detailed biophysical ones. As these rules are being proposed and developed by experimental and computational neuroscientists, engineers strive to design and implement them in silicon and en masse in order to employ them in complex real-world applications. In this paper, we describe analog very large-scale integration (VLSI) circuit implementations of multiple synaptic plasticity rules, ranging from phenomenological ones (e.g., based on spike timing, mean firing rates, or both) to biophysically realistic ones (e.g., calcium-dependent models). We discuss the application domains, weaknesses, and strengths of various representative approaches proposed in the literature, and provide insight into the challenges that engineers face when designing and implementing synaptic plasticity rules in VLSI technology for utilizing them in real-world applications.

Item ID: 45718
Item Type: Article (Research - C1)
ISSN: 1558-2256
Date Deposited: 06 Dec 2016 03:04
FoR Codes: 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080108 Neural, Evolutionary and Fuzzy Computation @ 33%
10 TECHNOLOGY > 1007 Nanotechnology > 100705 Nanoelectronics @ 34%
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 @ 33%
97 EXPANDING KNOWLEDGE > 970109 Expanding Knowledge in Engineering @ 34%
97 EXPANDING KNOWLEDGE > 970110 Expanding Knowledge in Technology @ 33%
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