  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.
