Neuroinformatics

Aapo HyvärinenProfessor, Group Leader

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Aapo Hyvärinen, professor, leaderPatrik Hoyer, academy research fellow, co-leaderMichael Gutmann, post-doc Ilmari Kurki, post-docKun Zhang, post-doc (11/2008-12/2009) 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

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Natural image statisticsBuild probabilistic models of natural images to model biological

vision Computational estimation theory

Computationally efficient estimation of probabilistic models

- Unnormalized models or latent variable modelsBrain imaging data analysis

Finding sources and their interactions in EEG/MEG dataCausal analysis (talk by D. Entner)

Analyze which variables are causes and which are effectsNon-Gaussian Bayesian networks / Structural equation models

Neuroinformatics Group: Projects

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Natural Image Statistics

First book on the subject

Published in June 2009

Combined textbook/monograph

Free preprint on the web

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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 θWorks 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

∫ p≠1

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 understandNeed data analysis methods to understand contentSomething like ICA should helpActually, 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 dataA spatial ICA using basic inverse problem solver

After inverse solution, many variablesTake transpose of data matrix

like with fMRIForce 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

columnsTwo 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

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After separating sources, analyze their interactionsConnections = correlations?We can find directions using time structure

or non-Gaussianity (causal inference)Clinical applications: schizophrenia, Alzheimer, etc.

Connectivity analysis

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Measure brain activity in two subjects when they are interacting

Find connectivity between the two subjectsCompletely new field

Data analysis method development definitely neededCollaborative 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

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Probabilistic methods with emphasis on computational aspects

Interface between informatics and statisticsTypically unsupervised learning

Discovery of hidden components, connections etc.Need also abstract theory of computationally efficient

estimation methodsApplications in many areas

We have special expertise in neuroscienceBrain imaging project going to be important in futureMoving towards more application-inspired research

Vision