High-dimensional Statistical Inference in the Brain

High-dimensional Statistical Inference in the Brain

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, 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 from thalamic to cortical representations 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 (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@rctn.org