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.
