BIONB 4380

Computational Methods for Neurobiology & Behavior

Course Description

Course information provided by the 2026-2027 Catalog.

This course is intended for advanced undergraduate and graduate students interested in computational methods for analyzing neural and behavioral data. Potential topics include signal processing, spectral analysis, information theory, cluster analysis, fitting parametrized models and maximum entropy models. We will also look at applications of modern machine learning techniques to scoring, analyzing and interpreting data. Although the course will be geared towards students who are new to computational methods, familiarity with calculus is recommended. Students will be asked to give a short oral presentation at the end of the semester on the use of methods from the class to analyze a real dataset.

Enrollment Priority Recommended prerequisite: basic calculus.

Distribution Requirements (BSC-AG, OPHLS-AG), (BIO-AS)

Last 4 Terms Offered 2024FA, 2021FA, 2019FA

Learning Outcomes

• Use Python (including the packages NumPy and SciPy) and Tensorflow to load and work with data.
• Work with vectors, matrices and tensors and know how to employ basic techniques from linear algebra.
• Use techniques from signal processing to filter data and compute power spectra and spectrograms.
• Work with probability distributions and perform independent component analysis.
• Have a basic grasp of modern techniques in machine learning that can be applied to data.

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