Neural network models

TL;DR

This paper reviews existing neural network models, introduces a new family with enhanced structure and dynamics for cognitive functions, and analyzes their properties through theoretical and numerical methods.

Contribution

It presents a novel family of neural network models with extended structure and dynamics aimed at improving cognitive capabilities.

Findings

New family of models with enhanced cognitive features

Analytical and numerical comparison with existing models

Discussion of limitations and future directions

Abstract

This work presents the current collection of mathematical models related to neural networks and proposes a new family of such with extended structure and dynamics in order to attain a selection of cognitive capabilities. It starts by providing a basic background to the morphology and physiology of the biological and the foundations and advances of the artificial neural networks. The first part then continues with a survey of all current mathematical models and some of their derived properties. In the second part, a new family of models is formulated, compared with the rest, and developed analytically and numerically. Finally, important additional aspects and any limitations to deal with in the future are discussed.