Complex Oscillatory Systems: Modeling and Analysis
Marie Curie European Joint Doctorate (MC-EJD)
Jan 2015 - Aug 2019
Many natural and artificial systems are often composed of oscillatory elements which, besides evolving according to their own non-trivial internal dynamics, mutually interact. As a result, many temporal and spatial scales are typically present, often accompanied by the spontaneous emergence of collective properties. Altogether, such features make the task of understanding the resulting evolution a challenging interdisciplinary problem. Zero-knowledge methods do generally require too large amount of data to allow drawing meaningful conclusions. In order to overcome this limitation, it is necessary to add skilful hypotheses about the structure of the underlying model and, thereby, on the relevant variables. This task is often tackled in an ad hoc way and the approach is based rather on personal preferences than on objective elements. The goal of this project is to fill the gap, by developing a general and coherent set of tools for the system identification and control, as well as to improve our ability to make predictions.
Complex Oscillatory Systems: Modeling and Analysis (COSMOS) was a Europe-wide consortium of 8 universities who together hosted 15 PhD students over 4 years. COSMOS has been made possible by a €3.9 M grant by the European Commission through its Marie Curie Initial Training Network scheme (Program H2020-EU.1.3.1, Grant agreement ID: 642563).
The Florence node of COSMOS was led by the three PIs Roberto Livi, Duccio Fanelli from the Department of Physics and Astronomy of the University of Florence (Network Theory) and Thomas Kreuz from ISC, CNR (Data Analysis) and included the two Early Stage Researchers (PhD students) Clement Zankoc and Eero Satuvuri for two years as well as for one year Pau Clusella, Maxime Lucas, and Irene Malvestio.
Neural Engineering Transformtive Technologies
Marie Curie Initial Training Network (MC-ITN)
Sept 2012 - Aug 2016
Neural Engineering is an inherently new discipline that brings together
engineering, biophysics and mathematics to design and develop brain-computer interface systems, cognitive computers and neural prosthetics.
Neural Engineering Transformative Technologies (NETT) was a Europe-wide consortium of 18 universities, research institutes and private companies who together hosted 17 PhD students and 3 postdoctoral researchers over 4 years. NETT has been made possible by a €5.3M grant by the European Commission through its Marie Curie Initial Training Network scheme (Program FP7-People-2011-ITN, Grant agreement ID: 289146).
The Florence node of NETT was led by the two PIs Alessandro Torcini (Network Theory) and Thomas Kreuz (Data Analysis) and included the Experienced Researcher (Postdoc) Mario Mulansky and the two Early Stage Researchers (PhD students) David Angulo Garcia and Nebojsa Bozanic.
Joint Israeli-Italian Laboratory for Neuroscience
Funded by the Italian Ministry of Foreign Affairs and International Cooperation (MAECI)
Jan 2010 - December 2019
Collaboration between the University of Tel Aviv, Israel, and the Institute of Complex Systems at the CNR in Florence, Italy. The Israeli part was lead by Prof. Eshel Ben-Jacob and Prof. A Barzilai, the Italian part by Dr. Antonio Politi, Dr. Alessandro Torcini and Dr. Thomas Kreuz. Dr. Paoli Bonifazio was hired as postdoc working with both sides.
Development of neuro-glia chips with applications in neuronal repair and robotics
Simulations of neuronal networks
Analysis of neuronal data.
Spike Time Dependent Plasticity
Marie Curie International Outgoing Fellowship (MC-IOF)
April 2007 - March 2009
To uncover the basic principles underlying the operation of the human brain is perhaps the enterprise of basic science with the most dramatic potential implications for the society even on a global scale. While advances in understanding the genetics and the physiology of the brain are essential, it is clear that the most profound revolution will come from a better understanding of how the human cortex processes information. We could learn about superior architectures for parallel information processing, a gain of knowledge immediately applicable in information technology. But even more important, in the health sector such understanding could lead to more efficient therapies against diseases related to neuronal malfunctions (such as Alzheimer, Epilepsy and Parkinson) eventually resulting in an immense reduction of the health care costs.
Dynamical Entropies in Assemblies of Neurons
Marie Curie Intra-European Fellowship (MC-IEF)
January 2005 - December 2006
The final aim of the present project will be to gain some comprehension of how the brain stores and processes information. By considering the brain as a high dimensional nonlinear system, we plan to apply information-theoretic and dynamical tools to characterize the response of single neurons as well as of neuronal assemblies to different classes of external (deterministic and stochastic) excitations.
Data measured in experiments and generated by simulating networks of various sizes will be examined and compared. The information content of the experimental and numerical time series will be estimated by computing dynamical entropies and mutual information for different variables and levels of resolution.