Seminar Computational Intelligence E (708.115)
Applications of Machine
Learning in Neurobiology
Grundlagen der Informationsverarbeitung (708)
Lecturer: O.Univ.-Prof. Dr. Wolfgang Maass
Office hours: by appointment (via e-mail)
Location: IGI-seminar room,
Inffeldgasse 16b/I, 8010 Graz
Date: Wednesday, 16:15-18:15 p.m.
starting on March 7, 2007 (organization meeting; you can also reserve a
talk by sending email to email@example.com
Content of the seminar:
We will discuss in this seminar the application of advanced methods
from machine learning and data mining (pattern classification,
feature extraction, clustering)
in order to detect patterns (e.g. "neural codes") in high-dimensional
time series as they emerge from various types of multi-unit recordings
in neurobiology (spike trains, local field potential, EEG).
We will focus on a few concrete study cases where our institute is
involved in the analysis of data from neurobiology, and will examine
the performance of various methods.
The available data and algorithms will be brought into a common format,
so that it will be easy to compare the performance of different
In addition we will have talks on some new approaches for pattern
recognition and feature extraction in machine learning, that promise to
provide improved performance.
The talks in this seminar can also serve as preparation for a project
or master thesis on applied machine learning.
Each talk will have a lengt of 40 minutes (in order to serve also as an
opportunity to extract and descibe the
most salient aspects of a paper).
Those among the papers listed below that are not publicly available,
can be found in our pdf-archive (students who are not working at our
institute can get a
copy from Angelika Zehetner firstname.lastname@example.org)
Tentative topics for talks
At our organization meeting we came up with the following plan:
Later in March (to be announced): Michael Pfeiffer and Martin Ebner
will talk about their analysis of the Cricket data from the KFU-team;
and Dr Hartbauer from the KFU will present details of these data.
21.05.2007 Michael Pfeiffer and Gregor Hoerzer:
Clustering (spectral clustering, and message-passing clustering)
23.05.2007 Martin Ebner
Methods developed for BCIs in Berlin, which may be relevant
for our data (in particular analysis of LFPs).
06.06.2007 (16.15 p.m.) Klaus Schuch
Sparse Bayesian Learning and Relevance Vector Machine
28.06.2007 (16.15 p.m.) Dejan Pecevski
Nonlinear feature selection with PSVM
28.06.2007 Stefan Klampfl
Methods for estimating mutual information developed by Stefano Panzeri
Remains to be scheduled:
Talk by Stefan Häusler on the data which he currently analyses
Talk by Robert Legenstein on his analysis of the data of Gregor Rainer
(some of the papers are quite long, and have to be split into several
-- Information contained in local field potential (LFP) and spike
trains of monkeys
-- Analysis of information contained in spike trains from the
Omega-neuron (= first stage of the auditory system) in
-- Separation of memory-induced and visually induced
information in the visual cortex of monkeys
-- Encoding of the percept of a visual stimulus
(for the case of ambigous images, where the visual input does not
change, but the internal percept in the brain changes)
-- Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Volker
Kunzmann, Florian Losch, Gabriel Curio, and Klaus-Robert Müller.
The berlin brain-computer interface: Machine-learning based detection
of user specific brain states. In Guido Dornhege, Jose del R.
Millán, Thilo Hinterberger, Dennis McFarland, and Klaus-Robert
Müller, editors, Towards Brain-Computer Interfacing. MIT
press, 2007. in press.
-- Classifying Single Trial EEG: Towards Brain Computer Interfacing ,
Benjamin Blankertz, Gabriel Curio, Klaus-Robert Müller
-- Hochreiter J. and Obermayer K. Support vector machines for dyadic
data. Neural Comput., 2006.
-- S. Hochreiter and K. Obermayer. Nonlinear Feature Selection with the
Potential Support Vector Machine. In: Feature extraction, Foundations
and Applications, Springer, 2005.
-- Arabzadeh E, Panzeri S, Diamond ME. Deciphering the spike train of a
sensory neuron: counts and temporal patterns in the rat whisker pathway.
J Neurosci. 2006 Sep 6;26(36):9216-26. Erratum in: J Neurosci. 2006 Sep
-- M.A. Montemurro, R. Senatore, S. Panzeri. Tight data-robust bounds
to mutual information combining shuffling and model selection
Neural Computation, in press
-- T. Natschläger and W. Maass. Dynamics
of information and emergent computation in generic neural microcircuit
models. Neural Networks, 18(10):1301-1308, 2005