Program in Computationally-Enabled Integrative Neuroscience (CEIN)
The department’s Program in Computationally-Enabled Integrative Neursoscience (CEIN) was created to train future neuroscientists to combine experimental methods and concepts, contemplate and build quantitative models that integrate empirical results from different levels of brain analysis, adhere to the highest standards of methodological and quantitative rigor, and have the professional skills and connection with human health.
This novel intergrative program maintains our core strength in training across levels of empirical analysis, from molecular and cellular neuroscience, to circuits and systems, to cognitive neuroscience. In addition, the program reflects significant evolution in our field: the increased importance of computation to analyze data and to bridge across those levels, and the increased importance of professional skills. As neuroscience is rapidly evolving, we have developed elements in this CEIN program to meet new challenges in training the next generation of leading neuroscientists.
The program is designed to achieve three objectives:
1) Broad training in research methods and concepts, and ability to integrate information across levels of neuroscience: The biggest questions in brain science require the ability to master methods, think across, and link multiple levels of empirical analysis. Our trainees will complete foundational coursework and supervised research (rotation projects) spanning multiple levels of analysis, advanced coursework, and independent, integrative research in a chosen mentor’s lab. Trainees will be evaluated on a demonstrated proficiency asking questions that span multiple levels of analysis.
2) Computational approaches and skills in the context of experiments and neuroscience data: Significant computational skills have become a necessity in neuroscience, and are an important catalyst for collaboration across disciplines. These skills include the ability to build and manage complex models (e.g. deep neural network models, recurrent network models, biophysical models) that explain and predict data, analyze complex datasets, test hypotheses about how observations at one level of empirical analysis are coupled to those at another level, and maintain the highest standards of statistical rigor and reproducibility. CEIN trainees will have access to a variety of computational and engineering courses (required and elective), a programming bootcamp and tutorials to review computational topics related to neuroscience.
3) Professional skills to lead and connect research with world impact: In addition to providing training in scientific and computational skills, our program has added new modules focused on professional skills required to be leading neuroscientists of the future. These modules include training in experimental design, data management, open sharing, oral communication, teaching, grant writing, and responsible conduct in science and teaching.
Our training program reflects our commitment to train future leaders in neuroscience in multiple experimental methods and concepts of neuroscience (“integrative neuroscience”), and in the use of computation at multiple levels of analysis including experimental design, data analysis, computational modeling, and theorizing. With comprehensive training in integrative neuroscience and computation trainees will be well prepared to make fundamental discoveries about the brain and to advance our understanding of neurological and psychiatric disorders.
To participate in the program, potential trainees must be in the first two years of the program. Candidates are identified by the Graduate Affairs Committee, based on their eligibility and academic record. Final student selections are based on merit and appropriate fit for the CEIN traineeship.
Upon completion of the training program, trainees will have completed all of the requirements listed below.
Mark Bear Edward Boyden Emery Brown Gloria Choi Kwanghun Chung Robert Desimone James DiCarlo Michale Fee Guoping Feng Steven Flavell John Gabrieli Ann Graybiel Michael Halassa Mark Harnett Myriam Heiman Alan Jasanoff Mehrdad Jazayeri Nancy Kanwisher Roger Levy Joshua McDermott Earl Miller Tomaso Poggio Alexander Rakhlin Rebecca Saxe Pawan Sinha Mriganka Sur Joshua Tenenbaum Susumu Tonegawa Li-Huei Tsai Matthew Wilson Feng Zhang