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Reconfigurable cellular neural netwo...

Yalcin, Mustak E.

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Reconfigurable cellular neural networks and their applications

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reconfigurable cellular neural networks and their applications/ by Mustak E. Yalcin, Tuba Ayhan, Ramazan Yeniceri.
作者:	Yalcin, Mustak E.
其他作者:	Ayhan, Tuba.
出版者:	Cham :Springer International Publishing : : 2020.,
面頁冊數:	vi, 74 p. :ill., digital ;24 cm.
內容註:	Introduction -- Artiﬁcial Neural Network Models -- Artiﬁcial Olfaction System -- Implementations of CNNs -- Index.
Contained By:	Springer eBooks
標題:	Neural networks (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-030-17840-6
ISBN:	9783030178406

Reconfigurable cellular neural networks and their applications
Yalcin, Mustak E.

Reconfigurable cellular neural networks and their applications[electronic resource] /by Mustak E. Yalcin, Tuba Ayhan, Ramazan Yeniceri. - Cham :Springer International Publishing :2020. - vi, 74 p. :ill., digital ;24 cm. - SpringerBriefs in nonlinear circuits,2520-1433. - SpringerBriefs in nonlinear circuits..

Introduction -- Artiﬁcial Neural Network Models -- Artiﬁcial Olfaction System -- Implementations of CNNs -- Index.

This book explores how neural networks can be designed to analyze sensory data in a way that mimics natural systems. It introduces readers to the cellular neural network (CNN) and formulates it to match the behavior of the Wilson-Cowan model. In turn, two properties that are vital in nature are added to the CNN to help it more accurately deliver mimetic behavior: randomness of connection, and the presence of different dynamics (excitatory and inhibitory) within the same network. It uses an ID matrix to determine the location of excitatory and inhibitory neurons, and to reconfigure the network to optimize its topology. The book demonstrates that reconfiguring a single-layer CNN is an easier and more flexible solution than the procedure required in a multilayer CNN, in which excitatory and inhibitory neurons are separate, and that the key CNN criteria of a spatially invariant template and local coupling are fulfilled. In closing, the application of the authors' neuron population model as a feature extractor is exemplified using odor and electroencephalogram classification.

ISBN: 9783030178406

Standard No.: 10.1007/978-3-030-17840-6doiSubjects--Topical Terms:

532070
Neural networks (Computer science)

LC Class. No.: QA76.87

Dewey Class. No.: 006.3

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