Cognitive Computation

, Volume 1, Issue 2, pp 119–127 | Cite as

Artificial Cognitive Systems: From VLSI Networks of Spiking Neurons to Neuromorphic Cognition

  • Giacomo IndiveriEmail author
  • Elisabetta Chicca
  • Rodney J. Douglas


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 Cognition Spike-based learning Winner-take-all Soft WTA VLSI 



This work was supported by the DAISY (FP6-2005-015803) EU Grant, by the Swiss National Science Foundation under Grant PMPD2-110298/1, and by the Swiss Federal Institute of Technology Zurich Grant TH02017404.


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Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • Giacomo Indiveri
    • 1
    • 2
    Email author
  • Elisabetta Chicca
    • 1
    • 2
  • Rodney J. Douglas
    • 1
    • 2
  1. 1.Institute of NeuroinformaticsUniversity of Zurich, ETH ZurichZurichSwitzerland
  2. 2.University of Zurich, ETH ZurichZurichSwitzerland

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