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Kavli Brain Forum

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Networks Thinking Themselves

2018-11-28 , Bassett, Danielle

Bassett's group studies biological, physical, and social systems by using and developing tools from network science and complex systems theory. Our broad goal is to isolate problems at the intersection of basic science, engineering, and clinical medicine that can be tackled using systems-level approaches. Recent examples include predicting the extent of learning from human brain networks, resolving the evolution of the neuronal synapse via genetic interaction networks, determining bulk material properties from mesoscale force networks, and isolating individual drivers of collective social behavior during evacuations. In these contexts, we seek to develop new mathematical methods for the principled characterization of temporally dynamic, spatially embedded, and multiscale networked systems, with the goal of predicting system behavior and designing perturbations to affect a specific outcome. A current focal interest of the group lies in network neuroscience. We develop analytic tools to probe the hard-wired pathways and transient communication patterns inside of the brain in an effort to identify organizational principles, to develop novel diagnostics of disease, and to design personalized therapeutics for rehabilitation and treatment of brain injury, neurological disease, and psychiatric disorders.

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Finding Language (and Language Learning) in the Brain

2018-08-29 , Fyshe, Alona

Understanding a native language is near effortless for fluent adults. But learning a new language takes dedication and hard word. In this talk, I will describe an experiment during which adult participants learned a new (artificial) language through a reinforcement learning paradigm while we collected EEG (Electroencephalography) data. We found that 1) we could detect a reward positivity (an EEG signal correlated with a participant receiving positive feedback) when participants correctly identified a symbol's meaning, and 2) the reward positivity diminishes for subsequent correct trials. Using a machine learning approach, we found that 3) we could detect neural correlates of word meaning as the mapping from native to new language is learned; and 4) the localization of the neural representations is heavily distributed throughout the brain. Together this is evidence that learning can be detected in the brain using EEG, and that the contents of a newly learned concept can be detected.

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