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Abstract:
Artificial neural networks are usually built on rather few elements
such as activation functions, learning rules, and the network
topology. When modelling the more complex properties of realistic
networks, however, a number of higher level structural principles
become important. In this paper, we present a theoretical framework
for modelling of cortical networks on a high level of
abstraction. Based on the notion of a population of neurons, this
framework can accommodate the common features of cortical
architecture, such as lamination, multiple areas and topographic
maps, input segregation, and local variations of the frequency of
different cell types (e.g., cytochrome-oxidase blobs). The framework
is primarily meant for the simulation of activation dynamics; it can
also be used to model the neural environment of single cells in a
multiscale approach.
