In the past decade, we have witnessed the rise of deep learning to dominate the field of
artificial intelligence. Advances in artificial neural networks alongside corresponding …

In the last decade, researchers, who work in the field of nonlinear circuits, have the “dream”
to use a real memristor, which is the only nonlinear fundamental circuit element, in a new or …

This tutorial paper proposes a subclass of cellular neural networks (CNN) having no inputs
(ie, autonomous) as a universal active substrate or medium for modeling and generating …

The field of cellular neural networks (CNNs) is of growing importance in non linear circuits
and systems and it is maturing to the point of becoming a new area of study in general …

For part I, see ibid., vol. 42, no. 10, pp. 602-11 (Oct. 1995). The general state equations
describing two-grid coupled CNNs based on the reduced Chua's circuit are derived, and the …

In the present paper, a spatially discretized diffusive Brusselator model with zero-flux
boundary conditions is considered. Firstly, the global existence and uniqueness of the …

In this paper a physiological-behavioral approach to neural processing is used to realize
artificial locomotion in mechatronic devices. The task has been realized by using a particular …

In this paper, a model for a network of neurons with reaction–diffusion is investigated. By
analyzing the linear stability of the system, Hopf bifurcation and Turing unstable conditions …

The p-adic cellular neural networks (CNNs) are mathematical generalizations of the neural
networks introduced by Chua and Yang in the 80s. In this work we present two new types of …

This book deals with locomotion control of biologically inspired robots realized through an
analog circuital paradigm as cellular nonlinear networks. It presents a general methodology …