ELEC380 / NEUR383 Syllabus - 2022

This course covers advanced statistical signal processing and machine learning approaches for modern neuroscience data (primarily many-channel spike trains). Topics include latent variable models, point processes, Bayesian inference, dimensionality reduction, dynamical systems, and spectral analysis. Neuroscience applications include modeling neural firing rates, spike sorting, decoding, characterization of neural systems, and field potential analysis.

Instructor: Caleb Kemere

Course Assistants:

Location: BRC282

Time: Mondays/Wednesdays 2-3:15 PM

Prerequisites:

  • E&M Phyics or basic circuits

  • Programming COMP140 or equivalent (i.e., CAAM210),

  • Python will be used for many of the homework assignments so some familiarity with scientific programming is critical.

Required Materials:

You will need a computer capable of running Python or Matlab. A laptop is preferred in order to allow you to participate in coding exercises during class.

Textbook:

No textbook is required, but some useful references include: - Theoretical Neuroscience by Dayan and Abbot. - Principles of Neural Science by Kandel, Koester, Mack, and Siegelbaum.

Objective:

Students should learn the fundamentals of how the activity of neurons represents information within in the brain, how this activity can be monitored experimentally, and how to decode underlying information from the resulting neural data. In addition, students should learn modern techniques for controlled perturbation of neural circuits.

Outcome:

Students completing the course should be able to:

  • Students are comfortable with neural data in many different forms, including “spikes” measured intracellularly, extracellularly, optically, and LFP/EEG.

  • Students are comfortable building generative models that describe neural activity either from first principles or using experimental data.

  • Students are comfortable using generative models to optimally decode underlying information from neural activity.

~Bi-Weekly Schedule:

  • Introduction and basic physiology of neurons

  • Signal processing in the time domain

  • Signal processing in the frequency domain

  • Signal processing in the spike domain

  • Neural decoding

  • Introduction to optogenetics and chemogenetics

  • Introduction to using intersectional genetics techniques

Grading:

Class grade will be based on homework assignments and the final project. You are welcome to work on homework in groups. One or two assignments may be given with instruction to complete individually. NOTE: The grading may be updated over the course of the semester.

  • 4-6 ~biweekly homework assignments (80%)

  • final project (20%)

  • in class quizzes (TBD)

Honor Code:

While students are encouraged to work together on homework assignments, there are two critical notes. First, all assignments – including code – should reflect the knowledge gained by the individual students. It is unacceptable that identical work should be turned in – at very least each student should have comments that reflect their own understanding. Second, if you develop an answer with other students, using online resources, or in consultation with the course staff, please note this in the assignment you turn in (there is no penalty for working together!). Plagarism may result in a zero score for an assignment.