<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><div><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div>Just a reminder: The application deadline for the Berkeley course in mining and modeling of neuroscience data, held July 11-22, 2016 is April 30 midnight.</div><div><div><br></div><div><br></div><div>Call for applications:</div><div> </div><div>We invite applicants to the 2016 summer course in</div><div>"Mining and modeling of neuroscience data" to be held July 11-22, 2016 at UC Berkeley.</div><div>A description of the course is below and also at:</div><div><a href="http://crcns.org/course">http://crcns.org/course</a></div><div><br></div><div>Berkeley summer course in mining and modeling of neuroscience data.</div></div><div dir="ltr"><br></div><div dir="ltr">July 11-22, 2016</div><div dir="ltr">Redwood Center for Theoretical Neuroscience, UC Berkeley</div><div dir="ltr">Organizers: Fritz Sommer, Bruno Olshausen & </div><div dir="ltr"> Jeff Teeters (HWNI, UC Berkeley)</div><div dir="ltr"><br></div><div dir="ltr">Scope</div><div dir="ltr">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 Institute of Health, the National Science Foundation from a grant supporting activities at the data sharing repository <a href="http://CRCNS.org">CRCNS.org</a>, and the Helen Wills Neuroscience Institute at UC Berkeley.</div><div dir="ltr"><br></div><div dir="ltr">Format</div><div dir="ltr">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 <a href="http://CRCNS.org">CRCNS.org</a> repository.</div><div dir="ltr"><br></div><div dir="ltr">Course Instructors</div><div dir="ltr">Robert Kass, Carnegie Mellon University, Pittsburgh</div><div dir="ltr">Maneesh Sahani, Gatsby Unit, University College London</div><div dir="ltr">Odelia Schwartz, University of Miami</div><div dir="ltr">Frederic Theunissen, University of California Berkeley</div><div dir="ltr">Joshua Vogelstein, Johns Hopkins University</div><div dir="ltr"><br></div><div dir="ltr">Course Moderators</div><div dir="ltr">Fritz Sommer and Jeff Teeters, Redwood Center for Theoretical Neuroscience.</div><div dir="ltr"><br></div><div dir="ltr">Speakers</div><div dir="ltr">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.</div><div dir="ltr"><br></div><div dir="ltr">Requirements</div><div dir="ltr">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 both MatLab and Python installed. </div><div dir="ltr"><br></div><div dir="ltr">Cost</div><div dir="ltr">There is no cost to attend. Non-local attendees will be reimbursed for economy travel expenses (up to a maximum, probably about $500) and also for qualifying food expenses if receipts are provided.</div><div dir="ltr"><br></div><div dir="ltr">Housing</div><div dir="ltr">Rooms in a nearby hotel will be provided for those attendees who need accommodations. </div><div dir="ltr"><br></div><div dir="ltr">Food</div><div dir="ltr">Breakfast and some dinners will be provided to all participants as a group. Non-local attendees can purchase food at local stores or restaurants and be reimbursed.</div><div dir="ltr"><br></div><div dir="ltr">How to apply</div><div dir="ltr">To apply, submit the online form linked from: <a href="http://crcns.org/course" target="_blank">http://crcns.org/course</a>. A curriculum vitae and a letter of recommendation are required. The course is limited to 25 students.</div><div dir="ltr"><br></div><div dir="ltr">Deadlines</div><div dir="ltr">Applications must be received by the end of April. Notifications of acceptance will be given by May 15.</div><div dir="ltr"><br></div><div dir="ltr">Questions</div><div dir="ltr">Questions about the course can be sent to course [at] <a href="http://crcns.org/" target="_blank">crcns.org</a>.</div><div dir="ltr"><br></div><div dir="ltr">Topics covered (subject to change):</div><div dir="ltr">Basic approaches:</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>The problem of neural coding</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Spike trains, point processes, and firing rate</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Statistical thinking in neuroscience</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Theory of model fitting / regularization / hypothesis testing</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Overview of stimulus-response function models</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Theory of model fitting / regularization / hypothesis testing</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Bayesian methods</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Spike sorting </div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Estimation of stimulus-response functionals: regression methods, spike-triggered covariance, </div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Variance analysis of neural response</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Estimation of SNR. Coherence</div><div dir="ltr">Information theoretic approaches:</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Information transmission rates</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Scene statistics approaches and neural modeling</div><div dir="ltr">Techniques for analyzing multiple-electrode and optical neural recordings:</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Event sorting in electrophysiology and optical imaging</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Optophysiology cell detection</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Sparse coding/ICA methods, vanilla and methods including statistical models of nonlinear dependencies</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Methods for assessing functional connectivity</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Statistical issues in network identification</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Low-dimensional latent dynamical structure in network activity–Gaussian process factor analysis/newer methods</div><div dir="ltr">Analyzing structural data:</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Histology cell detection</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Statistical connectomics</div><div dir="ltr">Neuroinformatics topics:</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Petascale data storage, exploration, parsing and analysis</div><div dir="ltr">-<span style="white-space:pre-wrap"> </span>Introduction into Neurodata Without Borders data format for Neurophysiology</div><div><br></div><div style="font-size:12.8px"><br></div></div>
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