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. 2018 Sep 15;16(3):A197-A202.
eCollection 2018 Summer.

Open Source Software Tools for Teaching Neuroscience

Benjamin Latimer  1 , David Bergin  2 , Vinay Guntu  1 , David Schulz  3 , Satish Nair  1
Affiliations

Open Source Software Tools for Teaching Neuroscience

Benjamin Latimer et al. J Undergrad Neurosci Educ. .

Abstract

We developed an interdisciplinary course in computational neuroscience to address the need for students trained in both biological/psychological and quantitative sciences. Increasingly, exposure to advanced math and physics is important to stay on the cutting edge of developments and research in neuroscience. Additionally, the ability to work in multidisciplinary teams will continue to be an asset as the field develops. This course brings together students from biology, psychology, biochemistry, engineering, physics, and mathematics. The course was designed to highlight the importance of math in understanding fundamental neuroscience concepts and to prepare students for professional careers in neuroscience. They learn neurobiology, via a 'biology to model and back again' approach involving wet- and software/modeling-labs, with the latter being the focus of this paper. We presented a subset of the software activities described here at the 2017 Faculty for Undergraduate Neuroscience Workshop.

Keywords: Computational Neuroscience; Electrophysiology; Software.

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Figures

Figure 1
Figure 1
The software and accompanying curriculum is freely available through our Canvas site. To download this software, simply go to courses.missouri.edu, scroll to the bottom of the page, and click “Canvas Guest & Visitor Login.” Enter username cns and password workshop to access the materials.
Figure 2
Figure 2
Student’s view of the Action Potential tutorial. The Hodgkin-Huxley equations are used to teach the concepts of voltage-dependent ion channels. Students can zoom in on graphs and explore any parameter in the model.
Figure 3
Figure 3
Virtual experimental setup of Software Experiment 4 (Bower and Beeman, 1998). Each synapse receives a user-determined excitatory (source A) or inhibitory drive (source B).
Figure 4
Figure 4
Central Pattern Generator of Software Experiment 5 (Bower and Beeman, 1998). The figure on the right is the membrane potential of each cell.
See this image and copyright information in PMC

References

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