[BIC-announce] OHBM 2020 Symposium announcement: Symposium title : Prospects in artificial intelligence neuroscience | Thursday 25th at 10 am

[Zografos Caramanos, Mr]

Dear all,

Please see attached the information about our OHBM 2020 Local Organizing Committee Symposium at the intersection of AI and Neuroscience, featuring Tal Arbel, Anna Schapiro and Blake Richards.
The event will start at 10am on Thursday, as part of the OHBM virtual conference<https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=3969> programme.

All the best,
JB


Symposium title : Prospects in artificial intelligence neuroscience

Organizers:
Jean-Baptiste Poline, Julien Doyon, Alan Evans and the Local Organizing Committee

Time: Thursday 25th, 10:00H New York, 15.00 London, 22.00H Hong Kong

OHBM 20202 online meeting<https://www.humanbrainmapping.org/i4a/pages/index.cfm?pageid=3969>


Speakers

Tal Arbel
Affiliations: McGill University
Title: Modelling and Propagating Uncertainties in Machine Learning for Medical Images of Patients with Neurological Diseases

Anna Schapiro
Affiliations: University of Pennsylvania
Title: Learning distributed representations in the human brain

Blake Richards
Affiliations: MILA and McGill University
Title: Mapping the brain with loss functions
This symposium is gathering three world class specialists at the intersection of neuroscience,
brain imaging, and artificial intelligence, giving the audience an update on three key research
directions. First, Tal Arbel will describe approaches for modelling and propagating uncertainties
in deep learning predictions from brain images of patients with neurological diseases, a crucial
aspect for the integration and buy-in of these techniques in a clinical context. Anna Schapiro will
follow with her recent empirical and neural network modeling work, inspired by human
neuroimaging insights, that asks the question: how do we learn distributed representations?
Last, Blake Richards will combine neuroscience and machine learning to map out different
pathways of the visual cortex using some of the fundamental building blocks of machine
learning.
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