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Paladyn, Journal of Behavioral Robotics

Editor-in-Chief: Schöner, Gregor

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2081-4836
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Computing Networks: A General Framework to Contrast Neural and Swarm Cognitions

Carlos Gershenson
  • Computer Sciences Department Instituto de Investigaciones en Matemáticas Aplicadas y en Sistemas Universidad Nacional Autónoma de Mexico Ciudad Universitaria, A.P. 20-726 01000 Mexico D.F. Mexico
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Published Online: 2010-09-09 | DOI: https://doi.org/10.2478/s13230-010-0015-z

Abstract

This paper presents the Computing Networks (CNs) framework. CNs are used to generalize neural and swarm architectures. Artificial neural networks, ant colony optimization, particle swarm optimization, and realistic biological models are used as examples of instantiations of CNs. The description of these architectures as CNs allows their comparison. Their differences and similarities allow the identification of properties that enable neural and swarm architectures to perform complex computations and exhibit complex cognitive abilities. In this context, the most relevant characteristics of CNs are the existence multiple dynamical and functional scales. The relationship between multiple dynamical and functional scales with adaptation, cognition (of brains and swarms) and computation is discussed.

Keywords: cognition; computation; neural architecture; swarm architecture; swarm cognition; multiple scales

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About the article

Received: 2010-07-26

Accepted: 2010-08-25

Published Online: 2010-09-09

Published in Print: 2010-06-01


Citation Information: Paladyn, Journal of Behavioral Robotics, ISSN (Online) 2081-4836, DOI: https://doi.org/10.2478/s13230-010-0015-z.

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© Carlos Gershenson. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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