Connectionists: NIPS 2013 Workshop Call For Papers: High-dimensional Statistical Inference in the Brain

[Stevenson, Ian]

             NIPS WORKSHOP 2013 CALL FOR PAPERS
       High-dimensional Statistical Inference in the Brain
                Monday, December 9th, 2013 
                  Lake Tahoe, Nevada USA
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Organizers:
 Mitya Chklovskii 
 Alyson Fletcher
 Fritz Sommer 
 Ian Stevenson
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Overview:
Understanding high-dimensional phenomena is at the heart of many
fundamental questions in neuroscience. How does the brain process
sensory data? How can we model the encoding of the richness of the
inputs, and how do these representations lead to perceptual
capabilities and higher level cognitive function? Similarly, the
brain itself is a vastly complex nonlinear, highly-interconnected
network and neuroscience requires tractable, generalizable models
for these inherently high-dimensional neural systems.

Recent years have seen tremendous progress in high-dimensional
statistics and methods for ``big data" that may shed light on
these fundamental questions. This workshop seeks to leverage these
advances and bring together researchers in mathematics, machine
learning, computer science, statistics and neuroscience to explore
the roles of dimensionality reduction and machine learning in
neuroscience. 

Call for Papers
We invite high quality submissions of extended abstracts on topics including,
but not limited to not limited to, the following fundamental questions:

-- How is high-dimensional sensory data encoded in neural systems?
What insights can be gained from statistical methods in dimensionality 
reduction including sparse and overcomplete representations? 
How do we understand the apparent dimension expansion in higher level 
cognitive functions from a machine learning and statistical perspective?

-- What is the relation between perception and high-dimensional statistical 
inference? What are suitable statistical models for natural stimuli 
in vision and auditory systems?

-- How does the brain learn such statistical models? What are the connections 
between unsupervised learning, latent variable methods, online learning 
and distributed algorithms? How do such statistical learning methods 
relate to and explain experience-driven plasticity and perceptual learning in 
neural systems?

-- How can we best build meaningful, generalizable models of the brain with 
predictive value? How can machine learning be leveraged toward better design 
of functional brain models when data is limited or missing? What role can 
graphical models coupled with newer techniques for structured sparsity play 
in this dimensionality reduction?

-- What are the roles of statistical inference in the formation and retrieval 
of memories in the brain? We wish to invite discussion on the very open 
questions of multi-disciplinary interest: for memory storage, how does the 
brain decode the strength and pattern of synaptic connections?  Is it 
reasonable to conjecture the use of message passing algorithms as a model? 

-- Which estimation algorithms can be used for inferring nonlinear and 
inter-connected structure of these systems? Can new compressed 
sensing techniques be exploited? How can we model and identify 
dynamical aspects and temporal responses?

We have invited researchers from a wide range of disciplines in electrical 
engineering, psychology, statistics, applied physics, machine learning 
and neuroscience with the goals of fostering interdisciplinary insights.  
We hope that active discussions between these groups can set in motion 
new collaborations and facilitate future breakthroughs 
on fundamental research problems.


Submissions should be in the NIPS_2013 format 
(include link http://nips.cc/Conferences/2013/PaperInformation/StyleFiles)
with a maximum of four pages, not including references.

Dates: 
Submission deadline: 23 October, 2013 11:59 PM PDT (UTC -7 hours)
Acceptance notification: 30 October , 2013

Web: http://users.soe.ucsc.edu/~afletcher/hdnips2013.html
email: hdnips2013 at rctn.org


Organizers:
Mitya Chklovskii,  HHMI Janelia Farm
Allie Fletcher,   UCSC
Fritz Sommer,   UC Berkeley
Ian Stevenson,  University of Connecticut

Confirmed Speakers
Liam Paninski,  Columbia University
Maneesh Sahani,  University College London 
Jonathon Pillow,   University of Texas
Surya Ganguli,  Stanford University
Matthias Bethge,  University of Tuebingen