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. 2017 Jun 15;15(2):A117-A121.
eCollection 2017 Spring.

An Algebra-Based Introductory Computational Neuroscience Course with Lab

Christian G Fink  1
Affiliations

An Algebra-Based Introductory Computational Neuroscience Course with Lab

Christian G Fink. J Undergrad Neurosci Educ. .

Abstract

A course in computational neuroscience has been developed at Ohio Wesleyan University which requires no previous experience with calculus or computer programming, and which exposes students to theoretical models of neural information processing and techniques for analyzing neural data. The exploration of theoretical models of neural processes is conducted in the classroom portion of the course, while data analysis techniques are covered in lab. Students learn to program in MATLAB and are offered the opportunity to conclude the course with a final project in which they explore a topic of their choice within computational neuroscience. Results from a questionnaire administered at the beginning and end of the course indicate significant gains in student facility with core concepts in computational neuroscience, as well as with analysis techniques applied to neural data.

Keywords: MATLAB; computational neuroscience; computer programming; neural modeling.

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Figures

Figure 1
Figure 1
Students’ self-reported agreement with three statements pertaining to their computer coding skills. Statements and response options shown in A, results shown in B, with horizontal lines indicating mean Likert values. The one-tailed Mann-Whitney U test was used to determine statistical significance (* = p<0.05, ** = p<0.01).
Figure 2
Figure 2
Students’ self-assessment of skill level with four fundamental data analysis techniques. Techniques and response options shown in A, results shown in B, with horizontal lines indicating mean Likert values. The one-tailed Mann-Whitney U test was used to determine statistical significance (* = p<0.05, ** = p<0.01).
Figure 3
Figure 3
Students’ self-reported agreement with five statements regarding their comprehension of information processing by neuronal systems. Statements and response options shown in A, results shown in B, with horizontal lines indicating mean Likert values. The one-tailed Mann-Whitney U test was used to determine statistical significance (* = p<0.05, ** = p<0.01).
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