Cybernetics c © Springer-Verlag 1996 Population networks: a large-scale framework for modelling cortical neural networks

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 num-ber of higher-level structural principles become important. In this paper we present a theoretical framework for mod-elling cortical networks at 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 meant primarily for the simulation of acti-vation dynamics; it can also be used to model the neural environment of single cells in a multiscale approach.