Population networks: a large-scale framework for modelling cortical neural networks |
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Authors: | Hanspeter A Mallot Fotios Giannakopoulos |
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Institution: | Max-Planck-Institut für biologische Kybernetik, Spemannstrasse 38, D-72076 Tübingen, Germany, DE Mathematisches Institut der Universit?t zu K?ln, Weyertal 86-90, D-50931 K?ln, Germany, DE
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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 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 activation
dynamics; it can also be used to model the neural environment of single cells in a multiscale approach.
Received: 9 January 1996 / Accepted in revised form: 24 July 1996 |
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