Specialization of deep learning architectures for modeling brain activity and function
Wednesday March 23, 2022 - 14.30
Nicola Toschi
University of Rome Tor Vergata
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Abstract: In the last 5 years, the advent of deep learning (DL) paradigms has revolutionized the field of artificial intelligence (AI). Previously, the contribution of AI across disciplines was confined to applications whose overall complexity is low, and towered by that of human physiology. However, modern DL paradigms offer unprecedented ability to integrate multimodal, multi-domain, and multiscale data with previously unimaginable prediction performance and little or no need for data preprocessing. With advent of ‘big’, publicly available, and often multidomain data repositories it is now possible to build and validate AI frameworks with a tangible potential to boost neuroscience research, enhance decision-making in disease management algorithm, and enrich next-generation clinical trials. This seminar will focus on the latest developments and deep architectures for modeling and studying biomedical data in general and brain structure and function in particular, as measured through e.g.. MEG, EEG, or MRI. Particular emphasis will be given to key architectural developments like e.g. separable convolutions, multi-head attention networks, spiking neural networks graph and contrastive learning. Every example will include a use-case stemming from an ongoing or published study.
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