[Neuroscience] CFP: NIPS 2011 Workshop on Machine Learnig and Interpretation in Neuroimaging (merged with "Interpretable Decoding of Higher Cognitive States from Neural Data")

Kai-min Kevin Chang via neur-sci%40net.bio.net (by kaimin.chang from gmail.com)
Mon Sep 26 09:26:19 EST 2011

Call for Papers


(NOTE: this workshop is now MERGED with the  NIPS workshop on "Interpretabl=
Decoding of Higher Cognitive States from Neural


December 16-17, 2011, Melia Sierra Nevada & Melia Sol y Nieve, Sierra
Nevada, Spain

Submission deadline (EXTENDED): * *October 17th, 2011


Modern multivariate statistical methods have been increasingly applied to
various problems in neuroimaging, including =93mind reading=94, =93brain ma=
clinical diagnosis and prognosis. Multivariate pattern analysis (MVPA) is a
promising machine-learning approach for discovering complex relationships
between high-dimensional signals (e.g., brain images) and variables of
interest (e.g., external stimuli and/or brain's cognitive states). Modern
multivariate regularization approaches can overcome the curse of
dimensionality and produce highly predictive models even in
high-dimensional, low-sample scenarios typical in neuroimaging (e.g., 10 to
100 thousands of voxels and just a few hundreds of samples).

However, despite the rapidly growing number of neuroimaging applications in
machine learning, its impact on how theories of brain function are construe=
has received little consideration. Accordingly, machine-learning techniques
are frequently met with skepticism in the domain of cognitive neuroscience.
In this workshop, we intend to investigate the implications that follow fro=
adopting machine-learning methods for studying brain function. In
particular, this concerns the question how these methods may be used to
represent cognitive states, and what ramifications this has for consequent
theories of cognition. Besides providing a rationale for the use of
machine-learning methods in studying brain function, a further goal of this
workshop is to identify shortcomings of state-of-the-art approaches and
initiate research efforts that increase the impact of machine learning on
cognitive neuroscience.

Decoding higher cognition and interpreting the behavior of associated
classifiers can pose unique challenges, as these psychological states are
complex, fast-changing and often ill-defined. For instance, speech is
received at 3-4 words a second; acoustic, semantic and syntactic processing
occur in parallel; and the form of underlying representations (sentence
structures, conceptual descriptions) remains controversial. ML techniques
are required that can take advantage of patterns that are temporally and
spatially distributed, but coordinated in their activity. And different
recording modalities have distinctive advantages: fMRI provides
millimeter-level localization in the brain but poor temporal resolution,
while EEG and MEG have millisecond temporal resolution at the cost of
spatial resolution. Ideally, machine learning methods would be able to
meaningfully combine complementary information from these different
neuroimaging techniques, and reveal latent dimensions in neural activity,
while still being capable of disentangling tightly linked and confounded

Moreover, from the machine learning perspective, neuroimaging is a rich
source of challenging problems that can facilitate development of novel
approaches. For example, feature extraction and feature selection approache=
become particularly important in neuroimaging, since the primary objective
is to gain a scientific insight rather than simply learn a ``black-box''
predictor. However, unlike some other applications where the set features
might be quite well-explored and established by now, neuroimaging is a
domain where a machine-learning researcher cannot simply "ask a domain
expert what features should be used", since this is essentially the questio=
the domain expert themselves are trying to figure out. While the current
neuroscientific knowledge can guide the definition of specialized 'brain
areas', more complex patterns of brain activity, such as spatio-temporal
patterns, functional network patterns, and other multivariate dependencies
remain to be discovered mainly via statistical analysis.

The list of open questions of interest to the workshop includes, but is not
limited to the following:

   - How can we interpret results of multivariate models in a
   neuroscientific context?
   - How suitable are MVPA and inference methods for brain mapping?
   - How can we assess the specificity and sensitivity?
   - What is the role of decoding vs. embedded or separate feature
   - How can we use these approaches for a flexible and useful
   representation of neuroimaging data?
   - What can we accomplish with generative vs. discriminative modelling?
   - How can ML techniques help us in modeling higher cognitive processes
   (e.g. reasoning, communication, knowledge representation)?
   - How can we disentangle confounded processes and representations?
   - How do we combine the data from different  recording modalities (e.g.
   fMRI, EEG, structural MRI, DTI, MEG, NIRS, EcOG, single cell recordings,

Workshop Format:

In this two-day workshop we will explore perspectives and novel methodology
at the interface of Machine Learning, Inference, Neuroimaging and
Neuroscience. We aim to bring researchers from machine learning and
neuroscience community together, in order to discuss open questions,
identify the core points for a number of the controversial issues, and
eventually propose approaches to solving those issues.

The workshop will be structured around 4 main topics:
       - Machine learning and pattern recognition methodology
       - Interpretable decoding of higher cognitive states from neural data
       - Causal inference in neuroimaging
       - Linking machine learning, neuroimaging and neuroscience

Each session will be opened by 2-3 invited talks, and an in depth
discussion. This will be followed by original contributions. Original
contributions will also be presented and discussed during a poster session.
Each day of the workshop will end with a panel discussion, during which we
will address specific questions, and invited speakers will open each segmen=
with a brief presentation of their opinion.

This workshop proposal is part of the PASCAL2 Thematic Programme on
Cognitive Inference and Neuroimaging (http://mlin.kyb.tuebingen.mpg.de/).

Paper Submission:

We seek for submission of original (previously unpublished) research papers=
The length of the submitted papers should not exceed 4 pages in Springer
format <http://www.springer.com/computer/lncs?SGWID=3D0-164-6-793341-0> (he=
are the  LaTeX2e style
excluding the references. We aim at publishing accepted paper after the
workshop in a proceedings volume that contains full papers, together with
short (5-page) review papers by the invited speakers. Authors are expected
to prepare a full 8 page paper for the final camera ready version after the

Submission of previously published work is possible as well, but the author=
are required to mention this explicitly. Previously published work can be
presented at the workshop, but will not be included into the workshop
proceedings (which are considered peer-reviewed publications of novel
contributions). Moreover, the authors are welcome to present their novel
work but choose to opt out of the workshop proceedings  in case they have
alternative publication plans.

Important dates:

- October 17th, 2011 - paper submission
- October 24th, 2011 -   notification of acceptance/rejection
- December 16th - 17th - Workshop in Sierra Nevada, Spain, following
the <https://sites.google.com/site/mlini2011/goog_1234450203>
NIPS <https://sites.google.com/site/mlini2011/goog_1234450203>
conference <https://sites.google.com/site/mlini2011/goog_1234450203>

Invited Speakers:

Maastricht, Netherlands)
Polina  <http://people.csail.mit.edu/polina/>Golland<http://people.csail.mi=
James <http://haxbylab.dartmouth.edu/ppl/jim.html>
V <http://haxbylab.dartmouth.edu/ppl/jim.html>.
Haxby <http://haxbylab.dartmouth.edu/ppl/jim.html> (Dartmouth College, US)
Tom <http://www.cs.cmu.edu/~tom/>
Daniel <http://www.doc.ic.ac.uk/~dr/>  <http://www.doc.ic.ac.uk/~dr/>
Rueckert <http://www.doc.ic.ac.uk/~dr/> (Imperial College, UK)
Peter <http://www.hss.cmu.edu/philosophy/faculty-spirtes.php>
Spirtes <http://www.hss.cmu.edu/philosophy/faculty-spirtes.php> (CMU, US)
Ga=EBl <http://gael-varoquaux.info/>

Program Committee:
Melissa Carroll <http://www.cs.princeton.edu/~mkc/> (Google, New York)
Guillermo Cecchi<https://researcher.ibm.com/researcher/view.php?person=3Dus=
T.J. Watson Research Center)
Kai-min Kevin Chang <kaimin.chang from gmail.com>, Language Technologies
Institute & Centre for Cognitive Brain Imaging, Carnegie Mellon University,
Pittsburgh, USA)
Moritz Grosse-Wentrup<http://www.kyb.tuebingen.mpg.de/nc/employee/details/m=
Planck Institute for Intelligent Systems, T=FCbingen)*
James V. Haxby <http://haxbylab.dartmouth.edu/ppl/jim.html> (Dartmouth
Georg Langs <http://people.csail.mit.edu/langs/Georg_Langs/index.html> (Med=
University of Vienna)*
Anna Korhonen <alk23 from cam.ac.uk> (Computer Laboratory & Research Centre for
English and Applied Linguistics, University of Cambridge)
Bjoern Menze <http://people.csail.mit.edu/menze/> (ETH Zuerich, CSAIL, MIT)
Brian Murphy <brian.murphy from unitn.it> (Computation, Language and Interaction
Group, Centre for Mind/Brain Sciences, University of Trento)*
Janaina Mourao-Miranda<https://iris.ucl.ac.uk/research/publication?upi=3DJM=
College London)
Vittorio Murino <http://profs.sci.univr.it/~swan/> (University of
Verona/Istituto Italiano di Tecnologia)
Francisco Pereira <http://www.princeton.edu/~fpereira/index.shtml> (Princet=
Irina Rish<http://domino.research.ibm.com/comm/research_people.nsf/pages/ri=
T.J. Watson Research Center)*
Mert Sabuncu <http://people.csail.mit.edu/msabuncu/> (Harvard Medical
Irina Simanova <irina.simanova from mpi.nl> (Max Planck Institute for
Psycholinguistics & Donders Institute for Brain, Cognition and Behaviour,
Bertrand Thirion
<http://parietal.saclay.inria.fr/Members/bertrand-thirion> (INRIA,

Primary contacts:

Moritz Grosse-Wentrup         moritzgw from ieee.org
Georg Langs                         langs from csail.mit.edu
Brian Murphy                        brian.murphy from unitn.it
Irina Rish                              rish from us.ibm.com

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