About this Journal Submit a Manuscript Table of Contents

Aims and Scope

Advances in Artificial Neural Systems publishes original research and authoritative reviews on all aspects of the engineering of artificial neural information processing based on the neural paradigm. The neural information processing paradigm rests on the use of large numbers of densely interconnected simple information processing units, and on highly parallel and distributed information processing throughout the system. Similar to natural information processing systems, artificial neural systems have a homogeneous structure, which are highly adaptive and integrate all the information processing for the task from sensing to actuation.

The goal of the journal is to stimulate the growth of knowledge about the structure, mechanisms and algorithms for artificial neural systems that can solve a complete information processing task, using directly the information as supplied by the environment. Such systems may consist of a combination of traditional artificial neural network modules such as feedforward, recurrent and self-organising neural nets. Model validation and performance evaluation of the described systems and their critical comparison with alternative existing solutions are considered essential components of any research in artificial neural systems.