Neural Signals and Computation
3.0
creditsAverage Course Rating
This course will go over the computational pipelines for recording and analyzing neural data at the population level. The first half of the course will cover core data processing steps, including spike-sorting and fluorescence imaging segmentation. The latter half will cover computational approaches to modeling neural populations, including dimensionality reduction and dynamical systems models. Both data-driven and theory-driven models will be considered, including sparse coding, predictive coding, RNNs, and others. Recommended Background: Linear Algebra, Probability and Statistics, Python or MATLAB programming.
Fall 2014
Professor: Andreas Andreou, Jeff Wang
Students’ favorite aspect of this class was the lab work where they gained hands-on experience. Students thought that the course’s greatest drawback was the lack of guidance they believed they received on assignments and the final project. They also believed that the exams did not align well with the material being taught. Students also felt the course would most benefit from updated course materials and lecture slides. Students thought it was important for potential participants to know that they course required a final project rather than a final exam.