Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

N. Mayer; J. M. Herrmann; T. Geisel

doi:10.1109/IJCNN.2000.859389

Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

N. Mayer, J. M. Herrmann, T. Geisel

School of Informatics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

High-dimensional models of pattern formation in visual cortex can be replaced by low-dimensional feature models provided that relations among the features reflect the high-dimensional structure. We consider orientation columns in a simplified flat high-dimensional setting and show that an exact derivation of a Riemannian-curved low-dimensional model is possible. Further evidence to the curved model is provided by the fact that the number of pinwheels is shown to stay non-zero in coincidence with finding in animals though in contrast to other models

Original language	English
Title of host publication	Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume:6 )
Publisher	Institute of Electrical and Electronics Engineers
Pages	153-158
Number of pages	6
ISBN (Print)	0-7695-0619-4
DOIs	https://doi.org/10.1109/IJCNN.2000.859389
Publication status	Published - 2000

Keywords / Materials (for Non-textual outputs)

brain models
self-organising feature maps
Riemannian-curved low-dimensional model
curved feature space
flat high-dimensional setting
low-dimensional feature models
orientation columns
pattern formation
visual cortex
Animal structures
Brain modeling
Computational efficiency
Computational modeling
Context modeling
Neurons
Numerical models
Pattern formation
Retina
Stationary state

Access to Document

10.1109/IJCNN.2000.859389

http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=859389

Cite this

Mayer, N., Herrmann, J. M., & Geisel, T. (2000). Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume:6 ) (pp. 153-158). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/IJCNN.2000.859389

Mayer, N. ; Herrmann, J. M. ; Geisel, T. / Structure formation in visual cortex based on a curved feature space : Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on. Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume:6 ). Institute of Electrical and Electronics Engineers, 2000. pp. 153-158

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title = "Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on",

abstract = "High-dimensional models of pattern formation in visual cortex can be replaced by low-dimensional feature models provided that relations among the features reflect the high-dimensional structure. We consider orientation columns in a simplified flat high-dimensional setting and show that an exact derivation of a Riemannian-curved low-dimensional model is possible. Further evidence to the curved model is provided by the fact that the number of pinwheels is shown to stay non-zero in coincidence with finding in animals though in contrast to other models",

keywords = "brain models, self-organising feature maps, Riemannian-curved low-dimensional model, curved feature space, flat high-dimensional setting, low-dimensional feature models, orientation columns, pattern formation, visual cortex, Animal structures, Brain modeling, Computational efficiency, Computational modeling, Context modeling, Neurons, Numerical models, Pattern formation, Retina, Stationary state",

author = "N. Mayer and Herrmann, {J. M.} and T. Geisel",

year = "2000",

doi = "10.1109/IJCNN.2000.859389",

language = "English",

isbn = "0-7695-0619-4",

pages = "153--158",

booktitle = "Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume:6 )",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

}

Mayer, N, Herrmann, JM & Geisel, T 2000, Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on. in Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume:6 ). Institute of Electrical and Electronics Engineers, pp. 153-158. https://doi.org/10.1109/IJCNN.2000.859389

Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on. / Mayer, N.; Herrmann, J. M.; Geisel, T.
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume:6 ). Institute of Electrical and Electronics Engineers, 2000. p. 153-158.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Mayer N, Herrmann JM, Geisel T. Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on. In Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on (Volume:6 ). Institute of Electrical and Electronics Engineers. 2000. p. 153-158 doi: 10.1109/IJCNN.2000.859389

Structure formation in visual cortex based on a curved feature space: Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Abstract

Keywords / Materials (for Non-textual outputs)

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