Artificial neural systems have been for the past thirty years a fascinating research topic with contributions from both theoreticians as well as from researchers implementing novel computational learning techniques into numerous application fields. Starting as an attempt to emulate human brain processing and cognition, supervised and unsupervised learning techniques as well as hybrid concepts have emerged for information visualization and discovery mining in high-dimensional spaces. The properties of these spaces are very different from what we usually encounter in the more intuitive low- dimensional spaces; the “curse of dimensionality,” for example, often impacts the performance of data-mining tools. However, with the increasing demand of information and knowledge processing, and integration of information from heterogeneous sources into biomedical decision tools and resources for health care, innovative learning approaches become imperative, especially when facing a huge number of data descriptors as it is the case in almost all applications in life sciences.

The aim of this special issue is to present the current state of the art in the theory of unsupervised learning and applications by active experts researching in the vast area of biosciences and medicine. We are interested in articles that explore both theoretical and application aspects of unsupervised learning techniques and in those that describe efficient visualization techniques in low-dimensional spaces. Potential topics include, but are not limited to:

• Self-organized maps, recurrent networks, mixture density networks, graph-based modeling, and kernel methods
• Unsupervised and reinforcement learning
• Novel concepts in learning algorithms such as information theory-related learning, semisupervised learning, ICC, BSS, and neural density approximation
• Feature selection and dimension reduction techniques
• Bioengineering
• Visualization in biomedicine
• Brain computer interface
• Neuroprosthetic devices
• Medical imaging and biomedical signal processing
• Computer-aided diagnosis (CAD) systems
• Bioinformatics and medical informatics
• Therapeutics and systems biology
• Models of the immune system