[Comp-neuro] MLINI 2016: NIPS Representation Learning in Artificial and Biological Neural Networks Workshop

Leila Wehbe leila.wb at gmail.com
Wed Sep 21 00:30:41 CEST 2016

NIPS Workshop on Representation Learning in Artificial and Biological
Neural Networks (MLINI 2016)
December 9th, 2016, Centre Convencions Internacional Barcelona, Barcelona,

*Call for papers and abstracts:*

Submission deadline: *Tuesday, September 27th, 2016*
Notification of acceptance: Wednesday, October 5th, 2016
Submission website: *https://cmt3.research.microsoft.com/MLINI2016
Workshop Website: https://sites.google.com/site/mlini2016nips

We invite submissions that are related, but not limited to:

   - Use of neural network and other methods as models of brain function
   - Machine learning methods, including deep learning, to analyze brain
   - Cognitively plausible learning algorithms, or in general models that
   take insights from human brains or behavior

We invite both:

   - Paper submissions, to be considered for online publication in arXiv
   proceedings, and poster presentation. The length should not exceed 6
   pages in Springer format
   <http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0> (here are
   the LaTeX2e style files
   excluding the references.
   - Abstract submission, to be considered for poster presentation. The
   abstract should not exceed 500 words (figures are allowed).

This workshop is in conjunction with a Frontiers topic entitled:

Artificial neural networks as models of human brain function


Participants are strongly encouraged to submit their work to the Frontiers
special topic edition (deadline is November 1st).

*About the workshop:*

This one day workshop is about the interface between cognitive neuroscience
and recent advances in AI fields that aim to reproduce human performance,
such as natural language processing or computer vision, and specifically
the deep learning approaches in these disciplines.

When studying the cognitive capabilities of the brain, scientists follow a
system identification approach in which they present different stimuli to
the subjects and try to model the evoked brain responses. The goal is to
understand the brain by trying to find the function that expresses the
activity of brain areas in terms of different properties of the stimulus.
Experimental stimuli are becoming increasingly complex with more and more
researchers studying real life phenomena such as the perception of natural
images or natural sentences. There is therefore a need for rich and
adequate representations of the properties of the stimulus, that can be
obtained using advances in NLP, computer vision or other relevant ML

In parallel, many new ML approaches, especially in deep learning, are
inspired to a certain extent by human behavior or biological principles.
Neural networks for example were originally inspired by biological neurons.
More recently, processes such as attention are being used which are
inspired by human behavior. However, the large bulk of these methods are
independent of findings about brain function, and it is unclear whether it
is at all beneficial for machine learning to try to emulate brain function
in order to achieve the same tasks that humans are capable of performing.

In order to shed some light on this difficult but exciting question, we
plan to bring together many experts from these seemingly converging fields
to discuss these problems, in a new highly interactive format consisting of
two short lectures from experts in both fields, followed by a guided

This workshop is a continuation of the Machine Learning and Interpretation
in Neuroimaging (MLINI) series. MLINI has already had 5 iterations in which
methods for analyzing and interpreting neuroimaging data were discussed in
depth. In keeping with tradition, we also invite contributions from the
expanding field of machine learning applied to neuroimaging data, and
specifically the recent trend of utilizing neural network models to analyze
brain data, which is evolving in parallel to the use of these algorithms as
models of the information content in the brain. This way we will complete
the loop: we will explore how neural networks and other machine learning
tools contribute to neuroscience, both as a source of models for brain
representations, and as a tool for brain image analysis.


Guillermo Cecchi (IBM T.J. Watson Research Center)
Moritz Grosse-Wentrup (Max Plank Institute for Intelligent Systems)
Georg Langs (Medical University of Vienna, CSAIL, MIT)
Brian Murphy (Queens University, Belfast)
Anwar Nunez-Elizalde (Helen Wills Neuroscience Institute, University of
California, Berkeley)
Irina Rish (IBM T.J. Watson Research Center)
Marcel van Gerven (Donders Institute for Brain, Cognition and Behaviour,
*Leila Wehbe (Helen Wills Neuroscience Institute, University of California,
Berkeley) - main contact
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