Introductory Computational Neuroscience

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Introductory Computational Neuroscience
These materials are a work in progress that can be used as the basis of an introductory computational neuroscience course that is intended to be a roughly 60/40 mixture of hands-on lab work and lecturing. The original one semester, 3 credit hour course (http://www.ni.gsu.edu/~rclewley/Teaching/CompNeuro/NEUR4030.html) was intended for mid-level undergraduates with either less rigorous math backgrounds or unfamiliarity with computer programming, or both. It is based mostly on Hugh Wilson's book, "Spikes, Decisions, and Actions", with some additional material from Izhikevich's book, "Dynamical systems in neuroscience". The course provides a visually- and conceptually-oriented introduction to the basics of qualitative analysis of differential equations and modeling neural systems. It focuses on electrophysiology of single neurons and activity in small networks, while trying to inspire interest in more sophisticated applications such as neural coding, cortical pattern formation and synchrony as components for memory and cognition, motor control, etc.
In particular, the scripts are modernized versions of the original Matlab codes from Wilson's book. They have been converted into Python, and in most cases take advantage of the ODE solving, phase plane, and bifurcation analysis capabilities of PyDSTool (http://pydstool.sf.net). The materials represent only a sample of the original codes, and in some cases are re-interpretations rather than direct translations. The codes are also not rigorously organized, but they will be a good starting point for new course development. The repository includes some homework and project ideas.
As a work in progress, the intention is for the materials to be customized or improved through community participation using the git version control system (http://youtu.be/SCZF6I-Rc4I). Feel free to fork and contribute to the project through github, and submit pull requests to update the Master version so that others may benefit from valuable contributions.
An index of the materials is provided in files_info.txt. The repository can be found at https://github.com/robclewley/compneuro.
Author Institutional Affiliation
Rob Clewley
Tutorial LevelBasic Tutorial
DescriptionTutorial
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