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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.
Contribution:
This paper demonstrates curricular modules that incorporate engineering model-based approaches, including concepts related to circuits, systems, modeling, electrophysiology, programming, and software tutorials that enhance learning in undergraduate neuroscience courses. These modules can also be integrated into other neuroscience courses.
Background:
Educators in biological and physical sciences urge incorporation of computation and engineering approaches into biology. Model-based approaches can provide insights into neural function; prior studies show these are increasingly being used in research in biology. Reports about their integration in undergraduate neuroscience curricula, however, are scarce. There is also a lack of suitable courses to satisfy engineering students' interest in the challenges in the growing area of neural sciences.
Intended Outcomes:
(1) Improved student learning in interdisciplinary neuroscience; (2) enhanced teaching by neuroscience faculty; (3) research preparation of undergraduates; and 4) increased interdisciplinary interactions.
Application Design:
An interdisciplinary undergraduate neuroscience course that incorporates computation and model-based approaches and has both software- and wet-lab components, was designed and co-taught by colleges of engineering and arts and science.
Findings:
Model-based content improved learning in neuroscience for three distinct groups: 1) undergraduates; 2) Ph.D. students; and 3) post-doctoral researchers and faculty. Moreover, the importance of the content and the utility of the software in enhancing student learning was rated highly by all these groups, suggesting a critical role for engineering in shaping the neuroscience curriculum. The model for cross-training also helped facilitate interdisciplinary research collaborations.
