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

Indiveri G, Chicca E, Douglas RJ (2009)
Cognitive Computation 1(2): 119-127.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
OA
Autor/in
; ;
Abstract / Bemerkung
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.
Stichworte
Neuromorphic engineering; Spike-based learning; Winner-take-all; Soft WTA; Cognition; VLSI
Erscheinungsjahr
2009
Zeitschriftentitel
Cognitive Computation
Band
1
Ausgabe
2
Seite(n)
119-127
ISSN
1866-9956
eISSN
1866-9964
Page URI
https://pub.uni-bielefeld.de/record/2426571

Zitieren

Indiveri G, Chicca E, Douglas RJ. Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation. 2009;1(2):119-127.
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. doi:10.1007/s12559-008-9003-6
Indiveri, G., Chicca, E., and Douglas, R. J. (2009). Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation 1, 119-127.
Indiveri, G., Chicca, E., & Douglas, R.J., 2009. Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation, 1(2), p 119-127.
G. Indiveri, E. Chicca, and R.J. Douglas, “Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition”, Cognitive Computation, vol. 1, 2009, pp. 119-127.
Indiveri, G., Chicca, E., Douglas, R.J.: Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition. Cognitive Computation. 1, 119-127 (2009).
Indiveri, G., Chicca, Elisabetta, and Douglas, R. J. “Artificial cognitive systems: From VLSI networks of spiking neurons to neuromorphic cognition”. Cognitive Computation 1.2 (2009): 119-127.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Volltext(e)
Access Level
OA Open Access
Zuletzt Hochgeladen
2019-09-06T08:57:58Z
MD5 Prüfsumme
4af780eca466c30aa8ac9e408efadc4e