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™
Standard for Systems and software engineering – Engineering and
management of websites for systems, software, and services
information, and IEEE P2145-1™ : 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.