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Integrating Model-Based Approaches into a Neuroscience Curriculum-An Interdisciplinary Neuroscience Course in
Benjamin Latimer1, David A Bergin2, Vinay Guntu1
1Electrical Engineering and Computer Science Department, University of Missouri, Columbia, MO 65211 USA.
IEEE Transactions on Education
|May 16, 2022
Summary
Engineering model-based approaches enhance neuroscience education for students and researchers. Integrating computation and software improves learning across undergraduate, Ph.D., and faculty levels, fostering interdisciplinary collaboration.
Area of Science:
- Neuroscience
- Computational Biology
- Engineering Education
Background:
- Growing call for integrating computation and engineering in biological sciences.
- Limited existing curricula for model-based approaches in undergraduate neuroscience.
- Need for courses bridging engineering and neural sciences.
Purpose of the Study:
- Demonstrate curricular modules using engineering model-based approaches for neuroscience.
- Enhance learning in undergraduate neuroscience courses.
- Facilitate interdisciplinary interactions and research.
Main Methods:
- Designed and co-taught an interdisciplinary undergraduate neuroscience course.
- Incorporated computation, model-based approaches, software tutorials, and wet-lab components.
- Modules covered circuits, systems, modeling, electrophysiology, and programming.
Main Results:
- Model-based content significantly improved neuroscience learning for undergraduates, Ph.D. students, and faculty.
- High ratings for content importance and software utility in enhancing learning.
- Facilitated interdisciplinary research collaborations through cross-training.
Conclusions:
- Engineering model-based approaches are crucial for advancing neuroscience education.
- Software and computational tools play a vital role in enhancing student learning.
- The developed model promotes interdisciplinary engagement and research in neural sciences.
