Dr. Simona Olmi

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    Istituto dei Sistemi Complessi - CNR, Sesto Fiorentino, Firenze, Italy

    Figure 1:

    MSc and PhD Dissertations

    • 2009 -- Laurea (MSc) in Theoretical Physics obtained at University of Firenze (Italy)

    Dynamics of diluted pulse coupled excitatory networks

    • 2012 -- PhD in Nonlinear Dynamics and Complex Systems obtained at the University of Firenze (Italy)

    Collective Dynamics in Complex Neural Networks


    • S. Olmi, R. Livi, A. Politi, and A. Torcini,

    Collective oscillations in disordered neural network Phys. Rev. E 81, 046119 (2010)

    • S. Olmi, A. Politi, and A. Torcini,

    Collective chaos in pulse coupled oscillators Europhys. Lett. 92, 6007 (2010)

    • L. Tattini, S. Olmi, and A. Torcini,

    Coherent periodic activity in excitatory Erdös-Reniy neural networks Chaos 22, 023133 (2012)

    • S. Luccioli, S. Olmi, A.Politi, and A. Torcini,

    Collective dynamics in sparse networks Phys. Rev. Lett. 109, 138103 (2012)

    • S. Olmi, A.Politi, and A. Torcini,

    Stability of the splay state in networks of pulse-coupled neurons J. Mathematical Neuroscience 2:12 (2012)

    • S. Olmi and A. Torcini, Coherent activity in excitatory pulse-coupled networks, Scholarpedia, 8 (10), 30928



    Research Experience

    During my undergraduate studies at the University of Florence, in Theoretical Physics, I attended courses mainly in Statistical Mechanics, Applied Mathematics and Fundamental Physics. However I started working on complex systems since my bachelor thesis (in 2005) when I studied chaotic and regular Hamiltonian systems, focusing on the perturbation theory of classical mechanics and integrable systems. In 2009, during my master thesis and, later, during my PhD (2010-2012), I implemented analytical and numerical methods borrowed from nonlinear dynamics to study complex networks such as pulse-coupled neural networks with different topologies. In particular I used simple mathematical models to describe neural network dynamics and nonlinear data analysis tools to characterize the emergent collective dynamics.

    The main aims of my PhD research were: (1) to investigated the role played by the topology in promoting coherent activity in pulse-coupled networks; (2) to understand if the onset of collective oscillations can be related to a minimal average connectivity and how this critical connectivity depends on the size of the networks; (3) to characterize the degree of chaoticity of these networks; (4) to investigate the emergence of peculiar states (e.g. collective chaos, chimera states) in neural networks; (5) to give general criteria for the stability of the “splay state” (asynchronous state) for oscillators (neurons) with generic velocity fields.

    I consolidated my education in Statistical Mechanics attending PhD courses at the Physics and Astronomy Department in Florence. In addition to this, by attending PhD courses at the Engineering Department in Florence I acquired a broad knowledge in Nonlinear Dynamics and Dynamical Systems Theory and I started learning Control Theory. Finally I attended programming courses on high level programming languages and on continuation theory softwares to improve my computational skills.

    I was a fellow student at the prestigious Summer School on Computational Neuroscience at the uOttawa Centre for Neural Dynamics, Ottawa, Canada. On the occasion of this summer school I had the opportunity to learn Matlab and to expand my background in Computational Neuroscience; in particular, developing a short project on Short Term Plasticity, I could analyze the effects of short term plasticity in inducing quasi synchronous events (in the form of population bursts) in a neural network of leaky integrate neurons with inhibitory and excitatory couplings. All in all, during the past three years of my PhD research I gained a quite multidisciplinary background in computational neuroscience, analysis of complex systems as well as network theory.

    Afterwards, during my first postdoc position, I started working on dynamical models of power grids, focusing my attention on complex phase models and multiscale networks. Furthermore I started collaborating with Prof. Boccaletti on the emergence of dynamical abrupt transitions in oscillator adaptive networks. In particular, given a set of phase oscillators networking with a generic wiring of connections and displaying a generic frequency distribution, I worked on finding practical weighting procedure which is able to induce and enhance explosive, irreversible, transitions to synchronization. The weighting procedure can be deduced by combining dynamical local information on frequency mismatches and global information on the graph topology. Moreover, I started collaborations with the Department of Physics and Astronomy at the University of Aarhus. This collaboration was related to the analysis of the influence of correlations among discrete stochastic excitatory or inhibitory spike trains on the response of a single leaky integrate-and-fire neuron model. In addition to this I started a collaboration with the PTB in Berlin with the goal in mind of analyzing conditions enabling self-organized synchronization in oscillator networks that serve as coarse-scale models for power grids. Recently I started working with Dr. E. A. Martens, Assistant Professor at the Department of Biomedical Science at Copenhagen University.

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