Interdisciplinary curriculum for computational neuroscience at primarily undergraduate institutions

Abstract

Developing interdisciplinary undergraduate courses is challenging at all levels; it requires involving trained faculty who can successfully cover more than one discipline and creating a new curriculum that crosses discipline boundaries. While such integrative curricula are expected in a graduate school setting, they are challenging at the undergraduate level since they require proficiency in multiple disciplines. However, making this transition to interdisciplinary, project-based teaching at the undergraduate level gives a competitive edge to undergraduates. As part of a broader effort of developing a comprehensive neuroscience curriculum, we implemented an interdisciplinary, one-semester, upper-level course called Biophysical Modeling of Excitable Cells (BMEC). The course exposes undergraduate students to broad areas of computational biology. It focuses on computational neuroscience, develops scientific literacy, and promotes teamwork between biology, psychology, physics, and mathematics-oriented undergraduates. This course also provides pedagogical experience for senior Ph.D. students in Neuroscience. BMEC is a three contact hours per week lecture-based course that includes a set of computer-based activities designed to gradually increase the undergraduates’ ability to apply mathematics and computational concepts to solving biologically-relevant problems. The class brings together two different groups of students with very dissimilar and complementary backgrounds, i.e., biology/psychology and physics/mathematics oriented. The teamwork allows students with more substantial biology/psychology backgrounds to explain to physics/mathematics students the biological implications and instill realism into the computer modeling project they completed for this class. Simultaneously, students with substantial physics/mathematics backgrounds can apply techniques learned in specialized mathematics, physics, or computer science classes to generate mathematical hypotheses and implement them in computer codes. This study expands on Oprisan (2021) by including examples of hands-on activities, student projects, and a brief overview of course assessment tools and results. This study also includes more recent approaches to teaching computational neuroscience using cloud computing.