NEURAL DATA SCIENCE
July 15 - 28, 2017
Application Deadline: April 15, 2017

Instructors:
Mark Reimers, Michigan State University                  Pascal Wallisch, New York University

See the Roll of Honor - who's taken the course in the past

Today’s technologies enable neuroscientists to gather data in previously unimagined quantities. This necessitates - and allows for - the development of new analysis methods to address dynamic systems function of brain networks. This course is designed to help neuroscience practitioners to develop the conceptual and practical capabilities to meet the challenges posed by the analysis of these hard-won and large data-sets. We will emphasize statistical issues such as the pre-processing of data, sampling biases, estimation methods and hypothesis testing as well as data wrangling (in MATLAB). We will work with data from a variety of recording technologies including single- and multi-electrode extracellular recordings, local field potentials and EEG as well as two-photon and wide-field optical imaging. The course will give a solid conceptual and technical grounding in widely applicable methods such as: spectral analysis, multivariate analysis, network inference as well as methods specific to each recording technique. We will also consider how to integrate neural data with behavioral data. 

The workshop will proceed in a seminar style, guided by leading neural data analysts, with demonstrations and practical lab data analysis exercises supervised by instructors.

The course is aimed primarily at advanced grad students and early postdocs, and will be held at the Laboratory’s Banbury Conference Center located on the north shore of Long Island. All participants stay within walking distance of the Center, close to tennis court, pool and private beach. The workshop will begin on the morning of July 15 (students are expected to arrive on the afternoon or evening of July 14) and end by lunchtime on July 28.