Mining and modeling of neuroscience data, July 9-20, 2012 Posted By: David Kennedy - Mar 16, 2012Tool/Resource: Conferences, Workshops and Meetings Call for applications: We invite applicants to the 2012 summer course in "Mining and modeling of neuroscience data" to be held July 9-20 at UC Berkeley. A description of the course is below and also at: http://crcns.org/course Application deadline is April 11. Berkeley summer course in mining and modeling of neuroscience data. July 9-20, 2012 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 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. 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 $384 for the entire two weeks per person in a shared double occupancy room (about $27.50 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/s... 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 April 11. Notifications of acceptance will be given by the end of April. If admitted, payment of $200 must be sent (postmarked by) by May 8. If using dorm housing, to guarantee a room, reservations must be made by June 8. 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 8). 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 --------------------------------------------------------------------------- Friedrich T. Sommer, Ph.D. Redwood Center for Theoretical Neuroscience & Helen Wills Neuroscience Institute UC Berkeley 575A Evans Hall, MC# 3198 Berkeley, CA 94720-3198 http://redwood.berkeley.edu/wiki/Fritz_S... |
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