This course is intended to provide an overview on the statistical, analytic, and computational tools that are commonly used in the research field of neuroscience. It will focus on characterizing the strength and applicability of various data analytical approaches in some more intuitive than formal ways. Through practical, simplified exercises, it aims to initiate students with tools that are likely to be useful for their future research in the field.

Years taught: 2021, 2022

• Two lecture hours each week
• Homework will be assigned for each textbook chapter covered
• Take home final in the last week

1. Advanced Data Analysis in Neuroscience: Integrating Statistical and Computational Models, Durstewitz, 2017. (Main)
   1. https://doi.org/10.1007/978-3-319-59976-2
   2. https://link.springer.com/book/10.1007%2F978-3-319-59976-2
2. Analysis of Neural Data, Kass, 2014. (Supplementary)
   1. https://link.springer.com/book/10.1007%2F978-1-4614-9602-1

Following the textbook the tentative outline is as following

• Statistical inference: What is the data telling us?
• Regression problems: Possible trends of the data.
• Classification problems: Asking questions and finding answers in the data.
• Model complexity and selection: Are we reading too much from the data?
• Clustering and density estimation: Characterize the “shapes” of the data.
• Dimensionality reduction: Cut out irrelevance, finding the most important.
• Linear time series analysis: Things that add up to the changing data.
• Nonlinear concepts in time series analysis: Chemistry between the causes of change.
• Time Series from a Nonlinear Dynamical Systems Perspective: The ways that moving things can mingle and what we will see as results.

Demonstration and implementation will be done in the Python programming language. Prior experience in Python will be helpful but not required.