Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.

Neurocomputing welcomes theoretical contributions aimed at winning further understanding of neural networks and learning systems, including, but not restricted to, architectures, learning methods, analysis of network dynamics, theories of learning, self-organization, biological neural network modelling, sensorimotor transformations and interdisciplinary topics with artificial intelligence, artificial life, cognitive science, computational learning theory, fuzzy logic, genetic algorithms, information theory, machine learning, neurobiology and pattern recognition.

Neurocomputing covers practical aspects with contributions on advances in hardware and software development environments for neurocomputing, including, but not restricted to, simulation software environments, emulation hardware architectures, models of concurrent computation, neurocomputers, and neurochips (digital, analog, optical, and biodevices).

Neurocomputing reports on applications in different fields, including, but not restricted to, signal processing, speech processing, image processing, computer vision, control, robotics, optimization, scheduling, resource allocation and financial forecasting.

Neurocomputing publishes reviews of literature about neurocomputing and affine fields.

Neurocomputing reports on meetings, including, but not restricted to, conferences, workshops and seminars.

Neurocomputing reports on functionality/availability of software, on comparative assessments, and on discussions of neurocomputing software issues.

Now also including: Neurocomputing Letters - for the rapid publication of special short communications.

Special Issue on Neural Networks for Game-Theoretic Intelligent Decision Making
Submission Date: 2023-11-30

In intelligent decision making, a decision maker must decide on what actions to execute in order to achieve goals in an environment. A decision maker (or an agent) can be artificial intelligence (AI) or a human. In recent years, significant improvements in neural networks and machine learning have spurred the usage of AI in many real-world decision-making problems, such as autonomous driving, gaming, and border testing against pandemics. The outcomes of these problems can depend on the decisions of a single agent, or the joint decisions of two or more agents. The former is traditional single-agent decision making, while the latter is the increasingly popular multi-agent decision-making setting.

Game theory provides a mathematical framework to model strategic interactions among multiple agents. The modern study of multi-agent decision making, thereby, often combines game theory, machine learning and neural networks. Neural networks empower decision making with efficient representation learning. On the other hand, game theory lays theoretical underpinnings and sheds light on the outcomes of multi-agent decision making.

The goal of this special issue is to promote collaboration and the cross-fertilization of ideas between researchers working from different aspects of game theory, intelligent decision making, machine learning and neural networks. We welcome both original theory- and application-driven contributions to this field, and we particularly encourage work in machine learning and neural networks for multi-agent decision making.