Dear Colleagues,

Computational intelligence, as a subset of artificial intelligence, is one of the most dynamically developing fields of computer science. This is due to the need to analyze increasingly larger data sets, which each time require the use of more efficient computer equipment and more advanced methods of data analysis and mining. One of the most powerful computational intelligence tools is artificial neural networks. These are structures inspired by the biological neural networks that make up the brain. A structure of this type learns to perform tasks thanks to given examples. At the present time, there is extensive interest in intelligent methods in application and theoretical fields. The above is visible by witnessing the continuous introduction of newer topological structures and teaching algorithms into this scientific domain. In particular, this dynamic is visible in the field of deep learning structures and procedures. Due to their specific plasticity and scalability, a tool called “Artificial Neural Networks” can be found to be employed in a very wide range of applications.

Artificial neural networks (ANNs) and their capability to address complex tasks with high efficiency when implemented on hardware have attracted remarkable interest for more than a decade now. The neuron models used in ANNs are highly simplified in function and operation to facilitate scaling to large networks.

This Special Issue will focus on “Neural Computation in Artificial Intelligence”, and we invite innovative and breakthrough ideas on implementing neural computation that goes beyond the limitations of the current ANN architectures. We aim to achieve features/functions not previously possible to perform new tasks and/or improve the performance in existing applications and thereby push the boundaries of what is possible.

Dr. Marta Chinnici
Prof. Dr. Pedro J. S. Cardoso
Prof. Dr. João M. F. Rodrigues
Guest Editors

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• neural networks
• convolutional neural networks
• logic for artificial intelligence
• fuzzy logic
• cloud computing
• nature of artificial intelligence
• nature-inspired predictions methods
• model interoperability
• reasoning about uncertainty
• machine learning
• data science
• (deep) reinforcement learning
• meta-learning
• natural language processing
• artificial neural network
• hybrid intelligent systems
• network architectures and learning paradigms
• fully connected networks
• affective computing