| prerequisites: A good working
knowledge of calculus, linear algebra, and basic
probability / statistics. Familiarity with python is also
desirable, as homework assignments and final projects will
involve programming. No prior experience with neural data is
required. |
| brief description: This course aims to
introduce students to methods for modeling and analyzing
neural datasets, with an emphasis on statistical approaches to
the problem of information processing in neural populations. A
tentative list of topics includes: neural encoding models,
Poisson processes, generalized linear models, logistic
regression, Gaussian processes, latent variable models, factor
analysis, mixture models, EM, Kalman filter, VAEs, latent dynamical
models of neural activity. The course is aimed at students
from quantitative backgrounds (neuroscience, engineering,
math, physics, computer science, statistics, psychology) who
are interested in the modeling and analysis of neural
data. |
| syllabus: pdf |
| Ed discussion: link
|
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