Neuroinformatics Aapo Hyvärinen Professor, Group Leader

2 Aapo Hyvärinen, professor, leader Patrik Hoyer, academy research fellow, co-leader Michael Gutmann, post-doc Ilmari Kurki, post-doc Kun Zhang, post-doc (11/ /2009)‏ Jun-ichiro Hirayama, visiting post-doc (1-12/2010)‏ Graduated PhD students: Urs Köster (12/2009), Jussi Lindgren(12/2008)‏ Current PhD students: Doris Entner, Antti Hyttinen, Miika Pihlaja, Jouni Puuronen Neuroinformatics Group: Members

3 Natural image statistics Build probabilistic models of natural images to model biological vision Computational estimation theory Computationally efficient estimation of probabilistic models -Unnormalized models or latent variable models Brain imaging data analysis Finding sources and their interactions in EEG/MEG data Causal analysis (talk by D. Entner)‏ Analyze which variables are causes and which are effects Non-Gaussian Bayesian networks / Structural equation models Neuroinformatics Group: Projects

4 Natural Image Statistics First book on the subject Published in June 2009 Combined textbook/monograph Free preprint on the web

5 New intuitive principle: train classifier to distinguish between your data and artificial noise If we use logistic regression with log p(x|θ) as regression function (AISTATS2010, poster on display) Provides consistent (convergent) estimator for θ Works directly for unnormalized models Generalized to a family which includes normalization using importance sampling (submitted)‏ Useful for complex models of natural images, e.g. 3 layers (COSYNE2010 poster on display)‏ Computational estimation theory MRF filters estimated from natural images

Brain imaging data analysis: Inverse problem in EEG/MEG Linear inverse problem: x=As with dim(x)<<dim(s)‏ A known from physics MEG data (x)‏ Estimated activity (s)‏ (Uutela, Hämäläinen, Somersalo, 1999)‏

Beyond the inverse problem Inverse solution transforms a 306 x 100,000 matrix to a 10,000 x 100,000 matrix Not easy to understand Need data analysis methods to understand content Something like ICA should help Actually, does ICA solve something like an inverse problem?

Blind source separation for MEG Improve BSS by taking short-time Fourier transforms as preprocessing (Hyvärinen, Ramkumar, Parkkonen, Hari, NeuroImage, 2010)‏ Takes into account the oscillatory nature of data A spatial ICA using basic inverse problem solver After inverse solution, many variables Take transpose of data matrix like with fMRI Force independence of spatial patterns, not time courses.

Combining inverse modelling with ICA Deep question: What is the connection between ICA and inverse problems? In both: x=As, x observed data ICA: A square, unknown Inverse problem: A known, but has many more columns Two ideas we're working on: Combine inverse modelling with ICA by constructing independent components in cortical space Use a prior on matrix A to make sources localized on the cortex

10 After separating sources, analyze their interactions Connections = correlations? We can find directions using time structure or non-Gaussianity (causal inference)‏ Clinical applications: schizophrenia, Alzheimer, etc. Connectivity analysis

11 Measure brain activity in two subjects when they are interacting Find connectivity between the two subjects Completely new field Data analysis method development definitely needed Collaborative project with Riitta Hari in the Computational Sciences program of the Academy of Finland Also the title of her ERC Advanced Investigator grant. Two post-docs starting in September Towards two-person neuroscience

12 Probabilistic methods with emphasis on computational aspects Interface between informatics and statistics Typically unsupervised learning Discovery of hidden components, connections etc. Need also abstract theory of computationally efficient estimation methods Applications in many areas We have special expertise in neuroscience Brain imaging project going to be important in future Moving towards more application-inspired research Vision