An historical discussion is provided of the intellectual trends that caused nineteenth century
interdisciplinary studies of physics and psychobiology by leading scientists such as …

Recent work on neural networks in chemistry is reviewed and essential background to this
fast-spreading method is given. Emphasis is placed on the back-propagation algorithm …

We show the formal equivalence of linearised self-attention mechanisms and fast weight
controllers from the early'90s, where a slow neural net learns by gradient descent to …

While scientists from different disciplines, such as neuroscience, medicine and high
performance computing, eagerly attempt to understand how the human brain functioning …

In this article, the Input-to-state stability theory is used to investigate the stochastic Cohen–
Grossberg bidirectional associative memory neural network with time-varying delay. In …

In this paper, a nonlinear Volterra integro-differential equation with Caputo fractional
derivative, multiple kernels, and multiple constant delays is considered. The aim of this …

In this paper, synchronization of an inertial neural network with time-varying delays is
investigated. Based on the variable transformation method, we transform the second-order …

The paper demonstrates that neural networks can be used effectively for the identification
and control of nonlinear dynamical systems. The emphasis of the paper is on models for …

Publisher Summary This chapter presents a survey of the elementary theory of the basic
backpropagation neural network architecture, covering the areas of architectural design …

Neural networks are a computing paradigm that is finding increasing attention among
computer scientists. In this book, theoretical laws and models previously scattered in the …