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Global scaling of synaptic efficacy: Homeostasis in silicon synapses


Bartolozzi, C; Indiveri, G (2009). Global scaling of synaptic efficacy: Homeostasis in silicon synapses. Neurocomputing, 72(4-6):726-731.

Abstract

Synaptic homeostasis is a mechanism present in biological neural systems that acts to maintain an homogeneous and stable computational substrate, in face of intrinsic inhomogeneities among neurons, and of their continuous changes due to learning processes and variations in the statistics of the input signals. In hardware spike-based neural networks homeostasis could be useful for solving issues such as mismatch and temperature drifts. Here we present a synaptic circuit that supports both spike-based learning and homeostatic mechanisms, and show how it can be used in conjunction with a software control algorithm to model global synaptic scaling homeostatic mechanism.

Synaptic homeostasis is a mechanism present in biological neural systems that acts to maintain an homogeneous and stable computational substrate, in face of intrinsic inhomogeneities among neurons, and of their continuous changes due to learning processes and variations in the statistics of the input signals. In hardware spike-based neural networks homeostasis could be useful for solving issues such as mismatch and temperature drifts. Here we present a synaptic circuit that supports both spike-based learning and homeostatic mechanisms, and show how it can be used in conjunction with a software control algorithm to model global synaptic scaling homeostatic mechanism.

Citations

12 citations in Web of Science®
14 citations in Scopus®
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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Uncontrolled Keywords:aVLSI; Neuromorphic; Synapse; Homeostasis; Spike-based learning; Synaptic scaling
Language:English
Date:January 2009
Deposited On:28 Feb 2010 10:44
Last Modified:05 Apr 2016 13:59
Publisher:Elsevier
ISSN:0925-2312
Free access at:Related URL. An embargo period may apply.
Publisher DOI:10.1016/j.neucom.2008.05.016
Related URLs:http://www.ini.uzh.ch/~giacomo/papers/pdf/neurocomputing09.pdf (Organisation)
http://www.ini.uzh.ch/node/19912

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