NEUR-3844: Computational Neuroscience and Neural Engineering
Description: Introduction to computational and systems neuroscience. Data analysis and signal processing techniques for neural data. Neural modeling to include mean field models, Hodgkin-Huxley models, integrate and fire models. Neural engineering and brain machine interface (BMI) applications.
Pathways: N/A
Course Hours: 3 credits
Sections Taught: 5
Average GPA: 3.16 (rounds to B+)
Strict A Rate (No A-) : 23.64%
Average Withdrawal Rate: 6.09%
| Sujith Vijayan | 2024 | 40.2% | 38.4% | 8.7% | 4.4% | 2.1% | 6.1% | 3.16 | 5 |