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Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain
Recent analyses combining advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons provide strong support for the hypothesis that neural dynamics operate near the edge of instability across regions in the brain.
The dynamics of neural codes in biological and artificial neural networks
This thesis combines three different approaches to the study of the dynamics of neural networks and their encoding representations: a computational approach, that builds upon basic biological features of neurons and their networks to construct effective models that can simulate their structure and dynamics; a machine-learning approach, which draws a parallel with the functional capabilities of brain networks, allowing us to infer the dynamical and encoding properties required to solve certain input-processing tasks; and a final, theoretical treatment, which will take us into the fascinating hypothesis of the "critical" brain as the mathematical foundation that can explain the emergent collective properties arising from the interactions of millions of neurons.
Optimal input representation in neural systems at the edge of chaos
Shedding light onto how biological systems represent, process and store information in noisy environments is a key and challenging goal.
Unveiling the role of plasticity rules in reservoir computing
Reservoir Computing (RC) is an appealing approach in Machine Learning that combines the high computational capabilities of Recurrent Neural Networks with a fast and easy training method.
Measles-induced immune amnesia and its effects in concurrent epidemics
In particular, the transitions from a quiescent state into these novel phases can become rather abrupt in some cases that we specifically analyze.
Statistical Mechanics Physics and Society Populations and Evolution
