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dc.contributor.authorRahimiazghadi, S.-
dc.contributor.authorIannella, N.-
dc.contributor.authorAl-Sarawi, S.-
dc.contributor.authorIndiveri, G.-
dc.contributor.authorAbbott, D.-
dc.identifier.citationProceedings of the Institute of Electrical and Electronics Engineers (IEEE), 2014; 102(5):717-737-
dc.description.abstractThe 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.-
dc.description.statementofresponsibilityMostafa Rahimi Azghadi, Nicolangelo Iannella, Said F. Al-Sarawi, Giacomo Indiveri, and Derek Abbott-
dc.rights© 2014 IEEE-
dc.subjectAnalog/digital synapse; Bienenstock–Cooper–Munro (BCM); calcium-based plasticity; learning; local correlation plasticity (LCP); neuromorphic engineering; rate-based plasticity; spike-based plasticity; spike-timing-dependent plasticity (STDP); spiking neural networks; synaptic plasticity; triplet STDP; very large-scale integration (VLSI); voltage-based STDP-
dc.titleSpike-based synaptic plasticity in silicon: design, implementation, application, and challenges-
dc.typeJournal article-
dc.identifier.orcidAl-Sarawi, S. [0000-0002-3242-8197]-
dc.identifier.orcidAbbott, D. [0000-0002-0945-2674]-
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Electrical and Electronic Engineering publications

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