Artificial intelligence(AI)based application systems are becoming the mainstream in the software industry and application domains such as computational neuroscience, bioinformatics, and healthcare. Recent advances in big data generation and management have created an avenue for decision makers to utilize these huge volumes of data for different purposes and analyses. AI-based application developers have long utilized conventional machine learning techniques to design better user interfaces and vulnerability predictions. However, with the advancement of deep learning-based and neural-based networks and algorithms, researchers are able to explore and learn more about data and their exposed relationships or hidden features. This new trend of developing data-driven application systems seeks the adaptation of computational neural network algorithms and techniques in many application domains, including software systems, cyber security, human activity recognition, and behavioral modeling. As such, computational neural networks algorithms can be refined to address problems in data-driven applications.

This Special Issue aims to foster machine and deep learning approaches to data-driven applications, in which data governs the behavior of applications. Original research and review articles that consider how to model and build data-driven applications using computational neuroscience are encouraged. More specifically, it is of interest to learn how neuron networks can represent data-driven applications and their behaviors in order to extract key features. The Special Issue will enable researchers from academia and industry to share innovative applications and creative solutions to common problems when modeling neural-based applications in computational neuroscience and neuroengineering.

Potential topics include but are not limited to the following:

• Neural programming and coding in data-driven systems using machine and deep learning
• Attention and learning of data-driven systems using neural memory networks
• Neural-based mathematical formulation of information and data using machine and deep learning
• Human perception, cognition, and decision making through neural networks
• Human activity recognition through deep neural modeling
• Modeling and prediction of human behavior using computational neuroscience techniques
• Modeling human-computer interactions through neural networks
• Modeling humanoid intelligent bots and mimicking human behavior using computational neuroscience techniques