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Low-rank methods for discovering structure in data tensors in neuroscience
To join this seminar online: https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2QyNzVlMDAtN2MyMi00MjFkLTk3NTgtZWI2MTcyNDVlOTEw%40thread.v2/0?context=%7b%22Tid%22%3a%22cc95de1b-97f5-4f93-b4ba-fe68b852cf91%22%2c%22Oid%22%3a%22e6ced614-5673-458c-832d-5d4ada66f593%22%7d
A fundamental question in neuroscience is to understand how information is represented in the activity of tens of thousands of neurons in the brain. Towards this end, low-rank matrix and tensor decompositions are commonly used to identify correlates of behavior in high-dimensional neural data. In this talk I will first present a novel tensor decomposition based on the slice rank which is able to disentangle mixed modes of covarying patterns in data tensors. Second, to compliment this statistical approach, I will present our recent dynamical systems modelling of neural activity over learning. Rather than factorizing data tensors themselves, we instead fit a dynamical system to the data, while constraining the tensor of parameters to be low rank. Together these projects highlight how applications in neural data can inspire new classes of low-rank models.
Date:
8 May 2025, 12:00
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Venue Details:
L3
Speaker:
Alex Cayco-Gajic (École Normale Supérieure Paris)
Organising department:
Mathematical, Physical and Life Sciences (MPLS)
Organiser:
Alain Goriely (University of Oxford)
Organiser contact email address:
Kirkham@maths.ox.ac.uk
Part of:
Industrial & Applied Mathematics Seminar
Booking required?:
Not required
Cost:
Free
Audience:
Public
Editor:
Nicola Kirkham
