Sutardja Dai Hall (SDH) Building, Meeting room 242
This full day working session brings together -but not only- researchers involved in the Inria@SiliconValley theme “Content & Supporting Learning Technologies“.
- Joke Durnez, Stanford University and Inria
- Nina Miolane, Inria and Stanford University, Consultant for Bay Labs
With recent advances in information technology, an ever increasing amount of data is being collected to better understand human cognition and health. With the staggering amount and size of the data, new questions arise. How can we adequately analyze these data? How can we summarise millions of time points in a structured and interpretable way? How can we use what we know about the computational brain in other contexts, such as artificial intelligence? How can we build new computational tools for medicine in order to significantly improve patients’ daily life?
Recent advances in the field, such as machine learning applications, present researchers with new opportunities, but come with new limits. In this workshop, we will give an overview of recently developed computational methods for the better understanding of the human brain, both in fundamental research as in profitable applications.
In the morning, we will present the most recent developments of (neuro) informatics tools by the ongoing Inria@SiliconValley collaborations. These collaborations include the Inria teams Asclepios, Athena, Magnet, Parietal and the californian institutions Berkeley, USC and Stanford. The morning session enables attendees to get a deeper understanding of the current Inria@SiliconValley projects, fostering further discussion and collaboration.
In the afternoon, the discussion will be expanded to the most recent breakthroughs based on ‘brain computing’ in the industrial world (part 1) and in academia (part 2). Leaders in neuroscience both from industry and academia will present their ongoing activities. We will hear from community efforts as well as from start-ups in the Silicon Valley, to grasp how these new technologies help better understanding of the brain.
Consultant for Bay Labs
|Exploring the brain anatomy with Geometric Statistics
LargeBrainNets Inria/ Stanford
|Anatomo-Functional Structure of the Visual Word Form Area: Combining Functional and Diffusion MRI
|10:40 – 11:00 BREAK
|Neuropowertools: improving statistical power in fMRI neuroimaging studies
|Task transferability via word embedding
|Issues on re-usability of statistical results in fMRI
|12:00 – 14:00 LUNCH
|Marion Le Borgne, John Naulty
|NeuroTechX: the international neurotechnology network
Verily, Google Life Science
|Saving brains faster with machine learning – time is brain
Inria, now Arterys
|Automating radiologists’ workflows with Deep Learning
|15:30 – 16:00 BREAK
|Motor induced sensory suppression in the brain
Berkeley, now Bay Labs
Modeling of natural stimulus representation in the human brain using canonical correlation analysis