BIONB 4380

Last Updated

  • Schedule of Classes - February 7, 2022 7:27PM EST
  • Course Catalog - February 7, 2022 7:14PM EST

BIONB 4380
Topics in Computational Methods for Neurobiology and Behavior

Course information provided by the Courses of Study 2021-2022.

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.

When Offered Fall.

Prerequisites/Corequisites Prerequisite: basic calculus is recommended.

Distribution Category (PBS-AS, BIO-AS)

Comments Offered in odd-numbered years only.

Outcomes
  • By the end of this class, students will be able to analyze real neurobiological and behavioral data using a suite of traditional and modern methods in computational neuroscience.

View Enrollment Information

Syllabi:

  •   Regular Academic Session. 

  • 3 Credits Graded

  •  3189 BIONB 4380   LEC 001

  •  
    • TR Corson-Mudd W358
    • Aug 26 - Dec 7, 2021

    • Ellwood, I

  • Instruction Mode: In Person
    Prerequisite: Basic calculus and programming experience are recommended. 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.