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ESyNN - a model to abstractly emulate synchronization in neural networks

Durer, H ; Waschulzik, T

9th International Conference on Artificial Neural Networks: ICANN '99, 1999, Vol.2 (470), p.791-796

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Título:
ESyNN - a model to abstractly emulate synchronization in neural networks
Autor: Durer, H ; Waschulzik, T
Assuntos: Brain ; Computer simulation ; Image recognition ; Neural nets ; Neurophysiology
É parte de: 9th International Conference on Artificial Neural Networks: ICANN '99, 1999, Vol.2 (470), p.791-796
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-2
ObjectType-Feature-1
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SourceType-Conference Papers & Proceedings-1
ObjectType-Conference-3
SourceType-Scholarly Journals-1
Descrição: A new neural network model is introduced that can represent multiple objects in the net at any one time. This is achieved by adding to the traditional neural net model an abstract description of activity correlation between neurons as postulated by von der Malsburg (1981). The biological system is used for inspiration and motivation of the model but no biological plausibility is intended. An example network shows that this model can still be used like traditional `only-activity' nets but with the added ability to process multiple percepts at the same time.
Editor: London: IEE
Idioma: Inglês

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