Name
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.2478/s13230-010-0015-z | Paladyn |
Keywords | Field | DocType |
swarm architecture,multiple scales.,swarm cognition,computation,neural architecture,cognition,ant colony optimization,cognitive ability,computer network,artificial neural network | Ant colony optimization algorithms,Particle swarm optimization,Swarm behaviour,Simulation,Computer science,Artificial intelligence,Artificial neural network,Cognition,Machine learning,Computation,Swarm robotics | Journal |
Volume | Issue | ISSN |
1 | 2 | 2081-4836 |
Citations | PageRank | References |
14 | 1.02 | 35 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carlos Gershenson | 1 | 392 | 42.34 |