Monday, September 23, 2013

[Comp-neuro] NIPS 2013 Workshop Call For Papers: High-dimensional Statistical Inference in the Brain

       High-dimensional Statistical Inference in the Brain
                Monday, December 9th, 2013
                  Lake Tahoe, Nevada USA
 Mitya Chklovskii
 Alyson Fletcher
 Fritz Sommer
 Ian Stevenson

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

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
with a maximum of four pages, not including references.

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


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  

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