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Class number:
3219
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Title: Computational Neuroscience |
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Department: Engineering |
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Career: Undergraduate |
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Component: Laboratory |
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Session: Regular |
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Instructor's Permission Required: No |
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Grading Basis: Regular |
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Units: 1.25 |
| Enrollment limited to 12 |
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Current enrollment: 6 |
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Available seats: 6 |
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Start date: Tuesday, September 5, 2023 |
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End date: Thursday, December 21, 2023 |
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Mode of Instruction: In Person |
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Schedule: R: 1:30PM-4:10PM, MECC - 320 |
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Instructor(s): Blaise, J. Harry |
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Prerequisite(s): Junior and senior STEM majors who have a C- or better in MATH 131 or permission of the instructor |
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Distribution Requirement: Meets Natural Science Requirement |
Course Description:
This course introduces students to computational neuroscience which represents an interdisciplinary science linking the diverse fields of neuroscience, biomedical engineering, computer science, mathematics and physics to study brain function. Through lectures, small classroom discussions and hands-on computer laboratory exercises, basic strategies for modeling single neurons and neuronal networks will be introduced, including cable theory, passive and active compartmental modeling, spiking neurons, and models of plasticity and learning. Neuronal modeling fundamentals such as the Nernst equilibrium, the Hodgkin-Huxley model and the Goldman equation will also be covered. There will be ample opportunities for students to design and simulate their own computational neuron models using computer-aided numerical simulation software packages, such as MATLAB and NEURON. |