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Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition


Indiveri, G; Chicca, E; Douglas, R J (2009). Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation, 1(2):119-127.

Abstract

Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology. Remarkable progress has been made recently, and complex artificial neural sensory-motor systems can be built using this technology. Today, however, NE stands before a large conceptual challenge that must be met before there will be significant progress toward an age of genuinely intelligent neuromorphic machines. The challenge is to bridge the gap from reactive systems to ones that are cognitive in quality. In this paper, we describe recent advancements in NE, and present examples of neuromorphic circuits that can be used as tools to address this challenge. Specifically, we show how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilities.

Neuromorphic engineering (NE) is an emerging research field that has been attempting to identify neural types of computational principles, by implementing biophysically realistic models of neural systems in Very Large Scale Integration (VLSI) technology. Remarkable progress has been made recently, and complex artificial neural sensory-motor systems can be built using this technology. Today, however, NE stands before a large conceptual challenge that must be met before there will be significant progress toward an age of genuinely intelligent neuromorphic machines. The challenge is to bridge the gap from reactive systems to ones that are cognitive in quality. In this paper, we describe recent advancements in NE, and present examples of neuromorphic circuits that can be used as tools to address this challenge. Specifically, we show how VLSI networks of spiking neurons with spike-based plasticity mechanisms and soft winner-take-all architectures represent important building blocks useful for implementing artificial neural systems able to exhibit basic cognitive abilities.

Citations

41 citations in Web of Science®
51 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:neuromorphic
Language:English
Date:2009
Deposited On:03 Mar 2010 18:44
Last Modified:05 Apr 2016 14:00
Publisher:Springer
ISSN:1866-9956
Additional Information:The original publication is available at www.springerlink.com
Free access at:Related URL. An embargo period may apply.
Publisher DOI:10.1007/s12559-008-9003-6
Related URLs:http://www.ini.uzh.ch/node/19826 (Organisation)
http://www.ini.uzh.ch/~giacomo/papers/pdf/cogcomp09.pdf (Organisation)

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