[Biography]
Francisco Herrera received the M.Sc. degree in Mathematics in 1988 and the Ph.D. degree in Mathematics in 1991, both from the University of Granada, Spain.

He is currently a Professor in the Department of Computer Science and Artificial Intelligence at the University of Granada. He has published more than 200 papers in international journals. He is coauthor of the book “Genetic Fuzzy Systems:Evolutionary Tuning and Learning of Fuzzy Knowledge Bases” (World Scientific, 2001).

As edited activities, he has co-edited five international books and co-edited twenty one special issues in international journals on different Soft Computing topics. He currently acts as Editor in Chief of the international journal “Progress in Artificial Intelligence (Springer) and serves as area editor of the Journal Soft Computing (area of evolutionary and bioinspired algorithms). He acts as associated editor of the journals: IEEE Transactions on Fuzzy Systems, Information Sciences, Advances in Fuzzy Systems, and International Journal of Applied Metaheuristics Computing; and he serves as member of several journal editorial boards, among others: Fuzzy Sets and Systems, Applied Intelligence, Knowledge and Information Systems, Information Fusion, Evolutionary Intelligence, International Journal of Hybrid Intelligent Systems, Memetic Computation, Swarm and Evolutionary Computation.

He received the following honors and awards: ECCAI Fellow 2009, 2010 Spanish National Award on Computer Science ARITMEL to the “Spanish Engineer on Computer Science”, International Cajastur “Mamdani” Prize for Soft Computing (Fourth Edition, 2010).

His current research interests include computing with words and decision making, bibliometrics, data mining, data preparation, instance selection, fuzzy rule based systems, genetic fuzzy systems, knowledge extraction based on evolutionary algorithms, memetic and genetic algorithms.

 [Abstract]
Metaplasticity concept was defined in 1996 by W.C. Abraham and presently is a biological concept widely known in the fields of biology and medicine: neuroscience, physiology, neurology and others. Inspired in it, outstanding improvements have been achieved in artificial neural networks design applied to pattern classification. The proposed training algorithm is inspired by the biological metaplasticity property of neurons and Shannon’s information theory. The concept is applicable to Artificial Neural Networks in general, although in this presentation it is centered on Multilayer Perceptrons (MLP). During the training phase, the Artificial Metaplasticity Multilayer Perceptron (AMMLP) algorithm gives higher values for updating the weights in the less frequent activations than in the more frequent ones. AMMLP achieves a more efficient training, while improving MLP performance. Tested in standard, well known and easy available Databases, its results are superior to the rest of algorithms, no matter what multidisciplinary application used as case study.

 [Biography]
Diego Andina (Prof. Dr.-Eng, IEEE Senior Member), was born in Madrid, Spain, were he received simultaneously two Master degrees, on Computer Science and on Electronics & Communications by the Technical University of Madrid (UPM), Spain, in 1990. He achieved the Ph D. degree in 1995 with a thesis on Artificial Neural Networks applications in Signal Processing. He presently works for UPM where he heads the Group for Automation in Signals and Communications (GASC/UPM), a research group interested in Signal Processing and Computational Intelligence Applications: Man-Machine Systems and Cybernetics. He is author or co-author of more than 200 national and international publications, being director of more than 50 R+D projects financed by National and Local Governments, European Commission or private Institutions and Firms. He is also Associate Editorial Member of several International Research Journals and Transactions, and has participated in the organization of more than 50 international Research, Innovation or Technology Transfer events.

 [Abstract]
Machine learning is about learning models from data. Bayesianmachine learning uses probability theory, in particular Bayes’ rule, to properly combine knowledge and data. I will show how the Bayesian paradigm helps to “read the brain”, leading to novel paradigms for brain-computer interfaces and image reconstruction from human brain activity.

 [Biography]
Dr Tom Heskes is a Professor in Artificial Intelligence,and he leads the Machine Learning Group, at the Institute forComputing and Information Sciences, Radboud University Nijmegen, theNetherlands. He is further affiliated Principal Investigator at theDonders Centre for Neuroscience.

Prof Heskes’ research is on artificial intelligence, in particular(Bayesian) machine learning. He works on Bayesian inference(approximate inference, hierarchical modeling, dynamic Bayesiannetworks, preference elicitation); machine learning (multi-tasklearning, bias-variance decompositions); and neural networks (on-linelearning, self-organizing maps, time-series prediction). In anutshell, he and the members of his group use probability calculus andstatistics to design and understand “intelligent” systems that canlearn from data. He is also involved in several projects that concernapplications in, among others, brain-computer interfaces andneuroimaging, adaptive personalization of hearing aids, automatedreasoning, and bioinformatics. Prof Heskes has published over 100research papers in the above area.

Prof Heskes is the Editor-in-Chief of Neurocomputing and AssociateEditor of five other journals. He has served in various committees ofover 50 international conferences since 2004 onwards.