Manuela La Rosa

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In this paper, systems formed by networks of simple nonlinear cells are studied. Using lattice models, some of the fundamental features of complex systems such as self-organization and pattern formation are illustrated. In the first part of this work, a lattice of identical Chua's circuit is used to experimentally study its global spatiotemporal dynamics,(More)
Magnetoencephalography (MEG) brain signals are studied using a method for characterizing complex nonlinear dynamics. This approach uses the value of d(infinity) (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure has been developed to extract this parameter from time series when the system's structure and laws are(More)
—Magneto Encephalographic (MEG) brain signals are studied using a method for characterizing nonlinear dynamics. This approach uses the value of d ∞ (d-infinite) to characterize the system's asymptotic chaotic behavior. A novel procedure was developed to extract this parameter from time series when the system's structure and laws are unknown. The(More)
In this paper, we investigate the role of topology on synchronization, a fundamental feature of many technological and biological fields. We study it in Hindmarsh-Rose neural networks, with electrical and chemical synapses, where neurons are placed on a bi-dimensional lattice, folded on a torus, and the synapses are set according to several topologies. In(More)
Nonlinear spatio-temporal analysis was performed on neural activity recorded with 148-channel whole-head Magnetoencephalography (MEG). The analysis consists of two phases: artifact removal and nonlinear feature evaluation. Known artifacts, produced by cardiac and eye movement, and unknown artifacts have been isolated from neural activity by using two(More)