Redefining information processing through neural computing models

Artificial Neural Networks (ANN) are currently exploited in many scientific domains. They had shown to act as doable, practical, and fault tolerant computational methodologies. They are equipped with solid theoretical background and proved to be effective in many demanding tasks such as approximating complex functions, optimizing search procedures, detecting changes in behaviors, recognizing familiar patterns, identifying data structures. ANNs computational limitations, essentially related to the presence of strong nonlinearities in the data and their poor generalization ability when provided of fully connected architectures, have been hammered by more sophisticated models, such as Modular Neural Networks (MNNs), and more complex learning procedures, such as deep learning. Given the multidisciplinary nature of their use and the interdisciplinary characterization of the problems they approach, ranging from medicine to psychology, industrial and social robotics, computer vision, and signal processing (among many others) ANNs may provide the bases for a redefinition of the concept of information processing. These reflections are supported by theoretical models and applications presented in the chapters of this book.