[Comp-neuro] Call for Abstracts: NIPS Workshop on Statistical Methods for Understanding Neural Systems

Jascha Sohl-Dickstein jns9 at cornell.edu
Sat Sep 5 00:58:45 CEST 2015


NIPS WORKSHOP 2015 CALL FOR ABSTRACTS

Statistical Methods for Understanding Neural Systems

Friday, December 11th, 2015

Montreal, Canada

--------

Organizers:  Allie Fletcher  Jakob Macke   Ryan Adams  Jascha Sohl-Dickstein

--------

Overview:

Recent advances in neural recording technologies, including calcium imaging
and high-density electrode arrays, have made it possible to simultaneously
record neural activity from large populations of neurons for extended
periods of time. These developments promise unprecedented insights into the
collective dynamics of neural populations and thereby the underpinnings of
brain-like computation. However, this new large-scale regime for neural
data brings significant methodological challenges. This workshop seeks to
explore the statistical methods and theoretical tools that will be
necessary to study these data, build new models of neural dynamics, and
increase our understanding of the underlying computation. We have invited
researchers across a range of disciplines in statistics, applied physics,
machine learning, and both theoretical and experimental neuroscience, with
the goal of fostering interdisciplinary insights. We hope that active
discussions among these groups can set in motion new collaborations and
facilitate future breakthroughs on fundamental research problems.

Call for Papers

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

How can we deal with incomplete data in a principled manner?

In most experimental settings, even advanced neural recording methods can
only sample a small fraction of all neurons that might be involved in a
task, and the observations are often indirect and noisy. As a result, many
recordings are from neurons that receive inputs from neurons that are not
themselves directly observed, at least not over the same time period. How
can we deal with this `missing data' problem in a principled manner? How
does this sparsity of recordings influence what we can and cannot infer
about neural dynamics and mechanisms?

How can we incorporate existing models of neural dynamics into neural data
analysis?

Theoretical neuroscientists have intensely studied neural population
dynamics for decades, resulting in a plethora of models of neural
population dynamics. However, most analysis methods for neural data do not
directly incorporate any models of neural dynamics, but rather build on
generic methods for dimensionality reduction or time-series modelling. How
can we incorporate existing models of neural dynamics? Conversely, how can
we design neural data analysis methods such that they explicitly constrain
models of neural dynamics?

What synergies are there between analyzing biological and artificial neural
systems?

The rise of ‘deep learning’ methods has shown that hard computational
problems can be solved by machine learning algorithms that are built by
cascading many nonlinear units. Although artificial neural systems are
fully observable, it has proven challenging to provide a theoretical
understanding of how they solve computational problems and which features
of a neural network are critical for its performance. While such ‘deep
networks’ differ from biological neural networks in many ways, they provide
an interesting testing ground for evaluating strategies for understanding
neural processing systems. Are there synergies between analysis methods for
analyzing biological and artificial neural systems? Has the resurgence of
deep learning resulted in new hypotheses or strategies for trying to
understand biological neural networks?

Confirmed Speakers:

Matthias Bethge

Mitya Chklovskii

John Cunningham

Surya Ganguli

Neil Lawrence

Guillermo Sapiro

Tatyana Sharpee

Richard Zemel

Workshop Website: h <http://users.soe.ucsc.edu/%7Eafletcher/hdnips2013.html>
ttps://users.soe.ucsc.edu/~afletcher/neuralsysnips.html
<https://users.soe.ucsc.edu/%7Eafletcher/neuralsysnips.html>

Email : smnips2015 at rctn.org

Submission details:

Submissions should be in the NIPS_2015 format (
http://nips.cc/Conferences/2015/PaperInformation/StyleFiles) with a maximum
of four pages, not including references. Submissions will be considered
both for poster and oral presentation. Submit at
https://cmt.research.microsoft.com/SMN2015/Protected/Author/ or via the
link on the workshop website.

Important dates:

Submission deadline: 10 October, 2015 11:59 PM PDT (UTC -7 hours)

Acceptance notification: 24 October, 2015
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://www.neuroinf.org/pipermail/comp-neuro/attachments/20150904/f67bf742/attachment.html>


More information about the Comp-neuro mailing list