[Comp-neuro] Berkeley course in mining and modeling neuroscience data, July 6-17, 2015

Jeff Teeters jteeters at berkeley.edu
Tue Feb 10 23:14:46 CET 2015

Call for applications:

We invite applicants to the 2015 summer course in
"Mining and modeling of neuroscience data"
to be held July 6-17 at UC Berkeley.
A description of the course is below and also at:
Application deadline is April 6 (or March 1, for MSRI's sponsored students).


Berkeley summer course in mining and modeling of neuroscience data

July 6-17, 2015
Redwood Center for Theoretical Neuroscience, UC Berkeley
Organizers: Fritz Sommer, Bruno Olshausen, Jeff Teeters (HWNI, UC
Berkeley); Christos Papadimitriou (Simons Institute, Berkeley);
Ingrid Daubechies (MSRI, Duke).

This course is for students and researchers with backgrounds in mathematics
and computational sciences who are interested in applying their skills
toward problems in neuroscience.  It will introduce the major open
questions of neuroscience and teach state-of–the-art techniques for
analyzing and modeling neuroscience data sets.  The course is designed for
students at the graduate level and researchers with background in a
quantitative field such as engineering, mathematics, physics or computer
science who may or may not have a specific neuroscience background. The
goal of this summer course is to help researchers find new exciting
research areas and at the same time to strengthen quantitative expertise in
the field of neuroscience. The course is sponsored by the National Science
Foundation from a grant supporting activities at the data sharing
repository CRCNS.org, the Helen Wills Neuroscience Institute, the Simons
Institute for the Theory of Computing and the Mathematical Science Research

The course is “hands on” in that it will include exercises in how to use
and modify existing software tools and apply them to data sets, such as
those available in the CRCNS.org repository.

Course Instructors
Vitaly Feldman, IBM Almaden Research Center
Sonja Grün, Juelich Research Center, Germany
Robert Kass, Carnegie Mellon University, Pittsburgh
Maneesh Sahani, Gatsby Unit, University College London
Odelia Schwartz, University of Miami
Eric Shea-Brown, University of Washington
Frederic Theunissen, UC Berkeley

Course Moderators
Fritz Sommer and Jeff Teeters, Redwood Center for Theoretical Neuroscience

To complement the main course instruction there will be lectures in the
evenings by local Berkeley and UCSF neuroscientists presenting their
research using quantitative approaches.

Applicants should be familiar with linear algebra, probability,
differential and integral calculus and have some experience using MatLab
and Python.  Each student should bring a laptop with the software installed.

There is no cost to attend.  Assistance for travel, housing and food costs
is in general not provided, (except for students who are sponsored by MSRI
or the Simons Institute; see below).

Potential financial support
Support for a limited number of students to attend will be provided through
both MSRI and the Simons Institute for the Theory of computing.  Applicants
who are at a MSRI partner institution can be admitted to the course and
receive support for attending through MSRI as described at:
Applicants with a Computer Science background are invited to apply for
support by the Simons Institute for the Theory of Computing and the
Computer Community Consortium (CCC).  See “How to Apply” section.

There may be some limited dorm housing available.  Cost is $742 per person
in a shared double room, which includes an all-you-can-eat buffet dinner on
evenings that meals are not supplied with the course.  We will help
coordinate sharing of dorm rooms for those who wish to stay in the dorms.
Other housing options include: a hostel near campus (
http://berkeleyhostel.com) which is $32 / night for a bed in shared dorm
room; nearby hotels (http://visitberkeley.com/stay); and accommodations
advertised through craigslist.org and airbnb.org. We will help students
setup groups to search for shared housing together.

Most meals are not included with the course, although breakfast items,
snacks and some dinners will be provided.  Food for other meals can be
purchased at the dorm cafeteria and local restaurants.

How to apply
There are two methods of applying.  Method 1) Students at a MSRI partner
institution can be admitted to the course and receive support for attending
through MSRI as described at:
Method 2)  All non-MSRI applications are submitted via the online form
linked from: http://crcns.org/course.   A curriculum vitae and a letter of
recommendation are required. Those applying using method 1 can also apply
using method 2 (in case the MSRI supported positions are all taken).
Applicants with a computer science background who wish to apply for support
from the Simons Institute for the Theory of Computing must apply using
method 2 and include additional information as instructed on the
application form.  The course is limited to 25 students.

Applications must be received by April 6 (or March 1, for MSRI’s sponsored
students).   Notifications of acceptance will be given by the end of April.
If staying in dorm housing, and not sponsored by MSRI or the Simons
Institute, payment for housing and the meal plan must be received one week
before the start of the course.

Questions about the course can be sent to course [at] crcns.org.

Topics covered
Basic approaches:
-   The problem of neural coding
-   Spike trains, point processes, and firing rate
-   Statistical thinking in neuroscience
-   Theory of model fitting / regularization / hypothesis testing
-   Bayesian methods
-   Spike sorting
-   Estimation of stimulus-response functionals:  regression methods,
spike-triggered covariance
-   Variance analysis of neural response
-   Estimation of SNR. Coherence
Information theoretic approaches:
-   Information transmission rates and maximally informative dimensions
-   Scene statistics approaches and neural modeling
Techniques for analyzing multiple-unit recordings:
-   Cross-correlation and JPSTH
-   Sparse coding/ICA methods, vanilla and methods including statistical
models of nonlinear dependencies
-   Unitary event analysis
-   Methods for detection of higher-order correlations
-   Correlation approaches for massively parallel spike trains
-   Proper surrogates for spike synchrony analysis
-   Methods for assessing functional connectivity
-   Advanced topics in generalized linear models
-   Low-dimensional latent dynamical structure in network activity–Gaussian
process factor analysis/newer methods
Towards building a theory of the brain:
-   Applications of mathematical analysis of dynamical systems in
-   Approaches based on methods from theoretical computer science
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