Computer Science – Neural and Evolutionary Computing
Scientific paper
2010-01-28
Paladyn, Journal of Behavioral Robotics 1(2): 147-153, 2010
Computer Science
Neural and Evolutionary Computing
18 pages, 5 figures
Scientific paper
10.2478/s13230-010-0015-z
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.
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