Interdisciplinary Training in Cognitive, Computational and Systems Neuroscience (CCSN)

Washington University, Saint Louis MO

Investigators

Linked publications & trials

Abstract

Over the past 30 years, the scope of systems neuroscience has expanded enormously. Functional brain imaging provided the opportunity to study the neural mechanisms of complex cognitive functions in humans. Concurrently, powerful techniques such as 2-photon microscopy, optogenetics, and high-density probes have made it possible to dissect neural circuits in animal models with unprecedented resolution. In parallel, computational approaches such as Bayesian models, dynamic systems, and deep neural networks became increasingly central to many research programs and to the field as a whole. Thus, systems neuroscience is increasingly interdisciplinary, integrating frameworks and techniques from molecular biology, neurophysiology, cognitive science, ethology, computer science, statistical physics, and more. In the face of this remarkable expansion, PhD programs face three challenges. First, the body of knowledge relevant to systems neuroscience has increased in breadth and depth. Yet, there is pressure on students to conduct research, publish, and get independent funding early in their career. This pressure translates into reducing the coursework and increasing time in the lab. Second, systems-level research is conducted in multiple departments and PhD programs. At WashU, these include the PhD programs in Neuroscience, Psychological & Brain Sciences, Biomedical Engineering – and more. Yet, students coming from different disciplines often do not speak each other’s language. Third, a successful career in science requires a sophisticated understanding of statistics and a broad portfolio of skills – writing papers and grant proposals, collaborating with colleagues with different scientific backgrounds, presenting results in scientific venues and to wider audiences, navigating the academic job market – that exceed the normal coursework. The Cognitive, Computational and systems neuroscience (CCSN) pathway was developed in response to these challenges. CCSN is an elite pathway available for graduate students in Training Years 3-4 and for post-docs in Training Years 1-2. The emphasis of CCSN is on interdisciplinary training, statistical education, and career development. CCSN students take foundational courses on cognitive science and animal behavior, systems neuroscience, and computational neuroscience. In addition, CCSN students and post-docs take semester-long courses that help them (a) learn best practices in data science and quantitative literacy and (b) develop a grant proposal aided by peer, instructor, and committee feedback. This proposal often develops into an actual NRSA application. In addition, CCSN trainees take part in Career Development activities including seminar presentations, organizing scientific events, hosting external speakers, participating in informal dinners with CCSN faculty, and community outreach events. CCSN has existed for ~20 years and has a demonstrated history of success. Here we request funds for 6 student and 2 post-doctoral fellowships. Contingent on the success of this application, WashU will provide matching funds for an additional 5 student slots.