Course

The course is described below and here: crcns-2011-course.pdf. The online application is at: https://crcns.org/course/apply.php.

Berkeley summer course in mining and modeling of neuroscience data.

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
Sonja Gruen, Institute for Neuroscience and Medicine INM-6, Research Center Juelich, Germany
         and RIKEN Brain Science Institute, Wako-Shi, Japan
Robert Kass, Carnegie Mellon University, Pittsburgh
Jonathan Pillow, University of Texas, Austin
Maneesh Sahani, Gatsby Unit, University College London
Odelia Schwartz, Albert Einstein College of Medicine
Frederic Theunissen, University of California, Berkeley

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.  These speakers, and their research areas are:  Jose Carmena, UC Berkeley: Brain-machine interfaces (BMI).  Yang Dan, UC Berkeley: Encoding and processing of visual information in the mammalian brain.  Walter Freeman, UC Berkeley: Developing dynamical theories of brain function using recordings from high-density electrode arrays.  Jack Gallant, UC Berkeley: Using fMRI and other data to understand the human visual system at a quantitative, computational level.  Mark Goldman, UC Davis: Deducing operation of networks of large numbers of interconnected neurons using single neuron measurements.  Jennifer Linden, University College London: Structure and function of cortex and sensory systems.  Bin Yu, UC Berkeley: Statistical machine learning and methodologies involving large data sets.
 
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
$800 for tuition.  Room and board not included.  Financial assistance may be available and must be requested on the application form.

Housing
Dorm housing is available.  The lowest rate is $384 for the entire two weeks per person in a double occupancy room (about $27.50 per night).  Details: The room rate is $64 per night or $384 per week (seven consecutive nights) for a single or double occupancy room.  Since the price of a double occupancy room is the same if one or two people are in it, sharing the room with someone will reduce the price per person to one half of the above.  We will help coordinate sharing of rooms for those who wish to do that.  Information about the dorm rooms is at: http://conferenceservices.berkeley.edu/summervis_index.html

Food
Meals are available in the dorm cafeteria and in local restaurants.  They are not included with the course.

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 April 5.  Notifications of acceptance will be given by the end of April.
If admitted, deposit of $300 must be made by May 9.  Remainder payment for the course ($500) is due May 31.  If using dorm housing, to guarantee a room, reservations must be made by May 31.  After that, reservations may be made on space available basis.  Payment for housing is made directly to the housing office when checking in (on July 10).

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