Scope
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 Institute.

Format
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

Speakers
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.

Requirements
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.

Cost
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:
http://www.msri.org/web/msri/scientific/workshops/summer-graduate-school
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.

Housing
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.

Food
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:
http://www.msri.org/web/msri/scientific/workshops/summer-graduate-school
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.

Deadlines
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
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 neuroscience
-   Approaches based on methods from theoretical computer science