Datensatz

Zusammenfassung

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.