Synchronization phenomena in large populations of interacting elements are the subject of
intense research efforts in physical, biological, chemical, and social systems. A successful …

Inspired by biological neurons, the activation functions play an essential part in the learning
process of any artificial neural network (ANN) commonly used in many real-world problems …

Artificial neural networks (ANNs) based machine learning models and especially deep
learning models have been widely applied in computer vision, signal processing, wireless …

Complex-valued neural networks Complex-Valued Neural Networks Page 2 IEEE Press 445
Hoes Lane Piscataway, NJ 08854 IEEE Press Editorial Board 2013 John Anderson, Editor in …

A model of a multivalued associative memory is presented. This memory has the form of a
fully connected attractor neural network composed of multistate complex-valued neurons …

In many studies, it is common to use binary (ie, unweighted) edges to examine networks of
entities that are either adjacent or not adjacent. Researchers have generalized such binary …

An associative memory network with local variables assuming one of q equidistant positions
on the unit circle (q-state phasors) is introduced, and its recall behavior is solved exactly for …

Networks of coupled oscillators have far-reaching implications across various fields,
providing insights into a plethora of dynamics. This review offers an in-depth overview of …

This article deals with the Lagrange stability (LS) of hypercomplex neural networks (HCNNs)
with time-varying delays. To overcome the non-commutativity and non-associativity of …

The mean-field equations of a Q-state clock neural network model are derived in the replica-
symmetric approximation using the replica method. These equations are studied for the …