\dm_csml_event_details UCL ELLIS

Machine Learning without human supervision on neuroscience signals


Alexandre Gramfort


Inria Parietal Team


Friday, 18 March 2022







Event series

DeepMind/ELLIS CSML Seminar Series


The revolution of artificial intelligence over the last decade has been made possible by statistical machine learning, and in particular by supervised learning where algorithms are given the labels associated with each observation. Although very efficient, this approach faces several difficulties in a neuroscience and more broadly in a medical context, one needs enough labels, one needs good labels, and sometimes simply defining what are the labels is problematic... While pure unsupervised learning can be tempting it leads to other kinds of difficulties, namely model selection, validation and results interpretation which is often challenging beyond computer vision and natural language processing. In my presentation, I will discuss recent strategies we have explored in my team to bring AI and neuroscience together by leveraging large EEG and fMRI datasets and without relying on tedious or costly human annotations. I will first present how self-supervised learning allows to reveal structures in EEG data [1], before explaining how fMRI and pretained language models can help us decipher language processing in the brain [2, 3]. Finally I will present how old ideas from latent factor models with independence assumptions can help us make sense of neuroimaging data collected when subjects are exposed to uncontroled naturalistic stimuli [4,5]. References [1] Banville, H., Chehab, O., Hyvärinen, A., Engemann, D. and Gramfort, A. (2020), Uncovering the structure of clinical EEG signals with self-supervised learning, J. Neural Engineering [2] Caucheteux, C, Gramfort, A, King, J.-R. (2021), Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects, Proc. EMNLP Findings, 2021 [3] Caucheteux, C, Gramfort, A, King, J.-R. (2021), Disentangling Syntax and Semantics in the Brain with Deep Networks, Proc. ICML [4] Richard, H., Gresele, L., Hyvärinen, A., Thirion, B., Gramfort, A., Ablin, P. (2020), Modeling Shared Responses in Neuroimaging Studies through MultiView ICA, Proc. NeurIPS [5] Richard, H., Ablin, P., Thirion, B., Gramfort, A., Hyvärinen, A., P. (2021), Shared Independent Component Analysis for Multi-Subject Neuroimaging, Proc. NeurIPS