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Berkeley summer course in mining and modeling of neuroscience data.

*** The course will not be held in 2014.  We are planning to start it again in 2015 ***

The 2013 course is described below and here: crcns-2013-course.pdf.



Berkeley summer course in mining and modeling of neuroscience data.


July 15-26, 2013
Redwood Center for Theoretical Neuroscience, UC Berkeley
Organizers: Fritz Sommer, Jeff Teeters

Scope
This course addresses 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 the 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 partially sponsored by the National Science Foundation from a grant supporting activities at CRCNS.org, which hosts a public repository of experimental neuroscience data.

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
Robert Kass, Carnegie Mellon University, Pittsburgh
Jonathan Pillow, University of Texas, Austin
Odelia Schwartz, Albert Einstein College of Medicine
Sonja Gruen, Institute for Neuroscience and Medicine Research Center, Juelich, Germany
Maneesh Sahani, Gatsby Unit, University College London
Tom Dean, Google

Speakers
To complement the main course instruction there will be lectures by neuroscientists from the San Francisco Bay Area 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 or other software for performing interactive mathematical computations (for example: Python or Mathematica).  MatLab is recommended because most exercises will be geared for MatLab.  Each student should bring a laptop with the software installed.

Cost
$200 for tuition.  Room and board not included.  Because of the low tuition cost, no financial assistance is available.

Housing
Dorm housing is available.  The lowest rate is $390 for the entire two weeks per person in a shared double occupancy room (about $28 per night).  To get this lowest rate, you must find someone to share the room with.  We will help coordinate sharing of rooms for those who wish to do that.  Private rooms are also available at twice the price.   Full information about the dorm rooms is at:
http://conferenceservices.berkeley.edu/summervis_index.html

Food
Meals are not included with the course, although some breakfast items and snacks will likely be supplied.  Food can be purchased at the dorm cafeteria and local restaurants.

How to apply
Apply at: http://crcns.org/course/apply.php.  A curriculum vitae and a letter of recommendation is required. The course is limited to 20 students.

Deadlines
Applications must be received by the end of April 10.  Notifications of acceptance will be given by the end of April.  If admitted, payment of $200 must be sent (postmarked by) by May 31.  If using dorm housing, reservations should be made by June 11.  Reservations are made on space available basis.  Payment for housing is made directly to the housing office when checking in (on July 14).

Questions

Questions about the course can be sent to course [at] crcns.org.  Email is preferred.  But you can also call Jeff Teeters at 510-642-7252.

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
-    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 and newer approaches
Tiny machines, big data – the future of neuroscience technology and its computational challenges:
-    Connectomics and beyond
-    Light gated ion channels
-    Brain Activity Map initiative

 

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