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Books > Computing & IT > Applications of computing > Artificial intelligence
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Reconfigurable Cellular Neural Networks and Their Applications (Paperback, 1st ed. 2020)
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Reconfigurable Cellular Neural Networks and Their Applications (Paperback, 1st ed. 2020)
Series: SpringerBriefs in Nonlinear Circuits
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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.
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