Brain & Mind Computational Breakfast

A monthly breakfast and venue for informal conversation about topics such as artificial intelligence, neuroscience, human behaviour, and digital humanities. Welcome!
NB. The December edition will be held on Dec 17.
BMC Seminar poster with speaker photos.

Next seminar: Tuesday, 8 October

Topics and speakers:

Using domain knowledge in machine learning models: a deep dive into linear 

Marijn van Vliet, Aalto University

Linear machine learning models are a powerful tool that can learn a data 
transformation by being exposed to examples of input with the desired output. 
But what if we don't have enough training data? I'm going to talk about how to 
help our model by giving it access to domain information. In order to do this, 
we must take a deep dive into how linear regression works.


Computational limits of clinical neuroscience

Prof. Tuukka Raij, Psychiatry, AoF clinical research fellow, HUS and Aalto NBE

I will present two computational challenges faced in the attempts to develop clinical tools for (personalized) neuropsychiatry. A classical challenge is poor replicability of neuroimaging studies that is partially related to the problem of multiple comparisons. Increasing availability of prior data in databases may allow using Bayesian prior images to weight correction for multiple comparisons, but to my knowledge, such methods wait to be established. A more recent challenge relates to predictive models, relying largely on machine learning, and hoped to help to predict outcome and to select optimal treatment at the individual level. It has been suggested that error of cross-validated prediction accuracy follows a binomial law, resulting in about +/- 20 % error bars in a typical-size brain-imaging study on 30 subjects (i.e. prediction accuracies below 70 % may equal tossing a coin). In addition to sample size, several factors contribute to the error that is difficult to estimate in a single study. Discussion between neuroscientists and computational scientist is needed to develop methods to estimate and minimize error in correction for multiple comparisons and in predictive models.


Dates and Speakers after October

12 November
Juha Salmitaival, Pauliina Ilmonen

17 December (note the date exception!)
Matti Hämäläinen, Timo Roine


NB! The talks begin at 9:30. Breakfast is served on a first-come-first-serve basis at 9:15.

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Past Talks

Brain & Mind Computational Breakfast

A monthly breakfast and venue for informal conversation about artificial intelligence, neuroscience, human behavior, digital humanities. Read more!

Department of Neuroscience and Biomedical Engineering
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