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AI for neuroscience—Methodology and Applications
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南方科技大学刘泉影助理教授,2023年3月22日于上海人工智能实验室 Recent advances in machine learning and deep learning have greatly enhancedthe potential of Al in neuroscience. Deep learning architectures providepowerful tools for analyzing structured data (such as brain images) andunstructured data (such as brain graph). in this talk, I will present our recentwork using Al models for neuroscience. 1) Al models are versatile for neuraldata processing, such as EEG artifact removal and data generation, as well asthe downstream tasks such as classification and regression. 2) The explainableAl techniques can mine neuroscience knowledge and uncover new scientificdiscoveries to explain the neural mechanisms of brain function and humanbehaviors. 3) Integrating neural information from the brain into Al frameworkwill help better information representation in Al model; in turn, Al can be asurrogate brain for synthetic data and virtual experiments in neuroscience.
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