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A hybrid analog/digital spike-timing dependent plasticity learning circuit for neuromorphic VLSI multi-neuron architectures


Mostafa, Hesham; Corradi, Federico; Stefanini, Fabio; Indiveri, Giacomo (2014). A hybrid analog/digital spike-timing dependent plasticity learning circuit for neuromorphic VLSI multi-neuron architectures. In: IEEE International Symposium on Circuits and Systems (ISCAS), Melbourne, Australia, 1 June 2014 - 5 June 2014, 854- 857.

Abstract

To endow large scale VLSI networks of spiking neurons with learning abilities it is important to develop compact and low power circuits that implement synaptic plasticity mechanisms. In this paper we present an analog/digital Spike-Timing Dependent Plasticity (STDP) circuit that changes its internal state in a continuous analog way on short biologically plausible time scales and drives its weight to one of two possible bi-stable states on long time scales. We highlight the differences and improvements over previously proposed circuits and demonstrate the performance of the new circuit using data measured from a chip fabricated using a standard 180nm CMOS process. Finally we discuss the use of stochastic learning methods that can best exploit the properties of this circuit for implementing robust machine-learning algorithms.

Abstract

To endow large scale VLSI networks of spiking neurons with learning abilities it is important to develop compact and low power circuits that implement synaptic plasticity mechanisms. In this paper we present an analog/digital Spike-Timing Dependent Plasticity (STDP) circuit that changes its internal state in a continuous analog way on short biologically plausible time scales and drives its weight to one of two possible bi-stable states on long time scales. We highlight the differences and improvements over previously proposed circuits and demonstrate the performance of the new circuit using data measured from a chip fabricated using a standard 180nm CMOS process. Finally we discuss the use of stochastic learning methods that can best exploit the properties of this circuit for implementing robust machine-learning algorithms.

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4 citations in Web of Science®
5 citations in Scopus®
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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:5 June 2014
Deposited On:25 Feb 2015 10:42
Last Modified:16 Aug 2017 00:16
Publisher:Proceedings of the 2014 IEEE International Symposium on Circuits and Systems (ISCAS)
Series Name:ISCAS
ISBN:978-1-4799-3431-7
Publisher DOI:https://doi.org/10.1109/ISCAS.2014.6865270

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