University of Birmingham > Talks@bham > Artificial Intelligence and Natural Computation seminars > A novel mathematical and computational paradigm to compute stability boundaries and bifurcations directly from data in closed-loop experiments

A novel mathematical and computational paradigm to compute stability boundaries and bifurcations directly from data in closed-loop experiments

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If you have a question about this talk, please contact Leandro Minku.

I will present a novel and unprecedented paradigm that enables to track unstable states and transitions between qualitatively different dynamics from noisy experimental data. These data are recorded from real-time computer-controlled closed-loop experiments such as dynamic-clamp in electrophysiology, closed-loop robots, HILL systems in electronics and hybrid testing in mechanical engineering. This technique does not assume any underlying model nor does it rely on inverse problems, however it suitably relies on the combine application of dynamical systems theory and feed-back control theory. This result opens new avenues of research, allowing the possibility of implementing intelligent closed-loop machine brain interfaces that allow to efficiently control and explain both normal and pathological brain states (e.g. Deep Brain Stimulation devices). Additionally other interfaces will efficiently communicate with central patterns generators and sensory systems (e.g. vision). Consequently, this result has great scope towards the medical community, clinical neuroscience, public health and theoretical neuroscience in general.

This talk is part of the Artificial Intelligence and Natural Computation seminars series.

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