Key words: Nonlinear signals & systems, Stochastic Resonance, Neural modeling.
I have developed during my university training, specific background in signal processing and complex systems study by modeling and simulation.
I have developed abilities in signal processing and analysis of biomedical data by doing a Master of Sciences on "Signal Processing applied in Biology and Medicine". This Master was completed by an internship at Institute of Theoretical Biology, University of Angers, France, on neurophysiological signals analysis. This internship was followed by a summer period at the Physiology Laboratory at University of Reno Nevada, USA, on ectrophysiological processing technics.
I have realized my Ph.D. work on stochastic resonance at Laboratory of Automatic Controled Systems Engineering, University of Angers, FRANCE. Stochastic resonance is a nonlinear effect that consists of the possibility of improving the transmission of a useful signal by addition of noise in some nonlinear systems. The phenomenon is currently studied in diverse fields in the international scientific community, especially in USA and in several European countries. I have contributed to develop a general theory of the phenomenon that I have confronted successfully with experimental implementations in electronic and optical devices, with a great efficiency giving many results published in international scientific journals.
I have especially contributed to explore an area of particular interest: the presence and the role of stochastic resonance in neural systems. Neural systems constitute a class of natural nonlinear systems which are very efficient for signal and information processing. So, it is especially important to understand the role that could be played by stochastic resonance in the nonlinear signal processing realized by neurons.