[Talks@IDSIA] TALK: Wednesday, Jan 28, h. 11:00 - Maurizio Fiasché, On the use of Quantum inspired Spiking Neural Networks and on their integration with chaotic measurements on EEG data for modelling epileptic brain

Wednesday, Jan 28, h. 11:00 at IDSIA Maurizio Fiasché, Politecnico di Milano (POLIMI) On the use of Quantum inspired Spiking Neural Networks and on their integration with chaotic measurements on EEG data for modelling epileptic brain Abstract. In the last 20 years a lot of works in literature analysed and proposed several methods capable to predict the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients. In spite of promising results presented, more recent evaluations could not reproduce these optimistic findings. This evaluation poses again the issue of the EEG of epileptic patients used for the prediction of seizures used in a joint way with other data (e.g. fMRI). Spatio- and spectro-temporal brain data (STBD) are the most commonly collected data for measuring brain response to external stimuli or to internal events such as epileptic seizures. Spiking neural networks (SNN) are brain-like connectionist methods, where the output activation function is represented as a train of spikes and not as a potential. This and other reasons do SNN models more biologically close to brain principles than any of the previous Artificial Neural Networks (ANN) methods, and for this reason, are presented as the third generation of ANN. In fact, they have great potential for solving complicated time-dependent pattern recognition problems defined by time series and STBD because of their inherent dynamical representation. Nevertheless several challenges have been reported in works in literature for SNN techniques studied. In this presentation we want to present a particular type of SNN, the evolving SNN (eSNN) with a Quantum inspired evolutionary technique for its weights, parameters and features optimization. In fact, the first challenge approached in in this presentation is the feature and parameter optimization in a SNN. A QiEA is a novel type of quantum-inspired evolutionary algorithm (QiEA) for feature and parameter optimization used here in a SNN. The QiEA is inspired by the multiple universes principle of quantum computing, such as a quantum bit and superposition of states. QiEA represents the most recent advance in the field of evolutionary computation thus, as second objective, the architecture of a QiESNN is presented here and proposed for pattern recognition on temporal brain data. The third objective is about the presentation of a framework where Chaotic measurement and the QiESNN presented before are used in an integrated way for better understanding the epileptic brain. This framework concept, architecture and applications are approached with several possible future evolutions. Maurizio Fiasché is research coordinator at the Department of Economics, Management and Industrial Engineering of Politecnico di Milano (POLIMI), where his research interests are in Computational Intelligence and ICT for manufacturing. He received a Ph.D. in Computer Engineering in 2010 and a M.Sc. in Electronical Engineering with magna cum laude in 2006 at University of Reggio Calabria, Italy. Member of INNS and Senior Member of IEEE in: Computational Intelligence, EMB, Signal Processing and Computer societies, He was involved in two IEEE WGs as member for his expertise: IEEE P23026^(TM) Standard for Systems and software engineering -- Engineering and management of websites for systems, software, and services information, and IEEE P2145-1^(TM) : Standard for Smart Transducer Interface for Sensors and Actuators - Common Network Services. He won a best paper award during ICONIP 2008 conference in Auckland, NZ. He is author of 37 papers in international journals and conference proceedings, member in Technical Program committee in about 50 International Conferences, and referee for top international Journal also including Elsevier Neural Networks, Neural Computing and IEEE Transactions. He has been involved as Senior Researcher in Social&Smart EU FP7 (FIRE Project) with University of Milan, in white'R FP7 Project, and in Touchplant Project of Regione Lombardia with Politecnico di Milano. Moreover he has been lecture for several Universities and Institutes in Electrotechnical, Statistical Inference, Computer Science and Signal Processing courses. He is also senior consultant as ICT Project Manager and Software Engineer for several national and multinational Companies and consulting Groups since 14 years.