Category theory applied to neural modeling and graphical representations

Resource type
Author/contributor
Title
Category theory applied to neural modeling and graphical representations
Abstract
Category theory can be applied to mathematically model the semantics of cognitive neural systems. Here, we employ colimits, functors and natural transformations to model the implementation of concept hierarchies in neural networks equipped with multiple sensors.
Date
2000
Proceedings Title
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
Conference Name
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
Place
Como, Italy
Publisher
IEEE
Pages
35-40 vol.3
Language
en
DOI
10/dr29pc
ISBN
978-0-7695-0619-7
Accessed
2019-11-22T17:36:08Z
Library Catalog
Crossref
Extra
ZSCC: 0000032
Citation
Healy, M. J. (2000). Category theory applied to neural modeling and graphical representations. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium (pp. 35–40 vol.3). Como, Italy: IEEE. https://doi.org/10/dr29pc
BIOLOGY, NEUROSCIENCE & PSYCHOLOGY
Methodology
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