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| During the last 30 years there has been a large development in experimental and theoretical approaches in neuroscience research. However, the assessment of neural dynamics, both in experimental and in modeling efforts, continues to be based to a large extent on a classical, but at the same time very limited, stimulus-response paradigm. In this paradigm a stimulus is deliver to a neural system, and the characteristics of the response are then studied offline in relationship to the given stimulus. In addition, the study of information processing in the nervous system is also often based on the assumption that neural mechanisms are well approximated by steady-state measurements of neural activity. However, transient states may, in many cases, better describe neural network activity. In this talk we will emphasize the need of closed-loop approaches to deal with transient neural dynamics. Our goal is to link new theoretical descriptions of transient neural dynamics with experiments that implement goal-driven closed-loop activity-dependent stimulation. Real-time stimulation feedback enables a wide range of innovative studies to characterize information processing with transient dynamics in neural networks, and to exploit the robustness, flexibility and capacity of this type of dynamics. |
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