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Teaching Computation in Neuroscience: Notes on the 2019 Society for Neuroscience Professional Development Workshop on
William Grisham1, Mathew Abrams2, Walt E Babiec3
1Department of Psychology, UCLA, Los Angeles, CA, 90095-1563.
Summary
This workshop explored teaching computational neuroscience, covering statistical foundations, programming languages like Python and MATLAB, and practical teaching resources. It aimed to equip educators with tools for effectively teaching computational skills in neuroscience.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Science Education
Background:
- The integration of computational approaches in neuroscience research necessitates effective pedagogical strategies.
- There is a growing need for educators to be proficient in teaching computational methods within neuroscience curricula.
Purpose of the Study:
- To review current tools, approaches, and examples for teaching computation in neuroscience.
- To provide educators with practical insights and resources for computational neuroscience education.
Main Methods:
- The content is based on presentations and discussions from the 2019 Society for Neuroscience Professional Development Workshop on Teaching.
- Expert talks covered statistical foundations, programming language comparisons (MATLAB vs. Python), computational methods, and curricular design.
Main Results:
- Key statistical foundations for data analysis in neuroscience were presented.
- MATLAB and Python were compared as pedagogical tools for teaching computational neuroscience.
- Practical aspects, training opportunities, and available resources for teaching computational modeling were discussed.
Conclusions:
- Effective teaching of computational neuroscience requires a solid understanding of statistical principles and programming tools.
- Curricular integration and practical, resource-based approaches are crucial for successful computational neuroscience education.
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