ELEC 548 / 483 Syllabus - 2021

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

Graders: Della Lu

Location: BRC284

Time: Tuesdays/Thursdays 10:50 - 12:05 AM

Prerequisites:

• Probability (ELEC 303 or equivalent),
• Linear algebra (Math 355 or equivalent),
• Python will be used for many of the homework assignments so some familiarity with scientific programming is critical.
• New for 2021 ELEC 478 - Intro to Machine Learning - or equivalent. (key topics: Classifiers, Clustering, Dimensionality Reduction)

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.

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.

Grading:

Class grade will be based on homework assignments and the final project. You are welcome to work on homework in groups. Students enrolled in 483 may work on their final projects in groups, but students enrolled in 548 are expected to work on them independently. NOTE: The grading may be updated over the course of the semester.

• 6-8 ~weekly homework assignments (70%)
• final project (30%) (students enrolled in 483 may do final project as a team)

~Bi-Weekly Schedule

1. Introduction
2. Fundamental Neurobiology
3. Modeling spike trains
4. Point processes
5. Classification
6. Clustering / Mixture models
7. Continuous Decoding
8. Spectral Analysis