Pre-start
Announcements
- Office hours tmrw morning as usual
- General note
- Hw 3 Note
- Cool stuff!
- Hws 5 &6 will be merged and shortened-ish plus one NEW :D question
- Final project
- Hard out at the end of class. I need to go steal some candy from 12:20 need to be at Mudd
Neural Signal Processing & Machine Learning
ELEC/BIOE 548 | ELEC 483
Fall 2022
Episodes 21 & 22 Dimensionality Reduction
1
Introduction. Class & brains
2
Fundamental neurobiology. How do neurons fire? How/what do we record?
3
Modeling spike trains. First bit of analysis work and understanding firing properties of neurons.
5
Classification. Making machines learn. Which direction is a monkey trying to reach? Bayesian decoding.
4
Point processes. Continued modeling work of neurons.
6
Clustering/Mixture models. Making machines learn some more. Spike sorting.
Bi-weekly Schedule
7
Continuous decoding. Kalman filters. Machines continue to learn.
8
Spectral analysis? LFP interpretation in spectral domain. But also kinda in clustering.
Brain Signals!
-
How can we measure neural activity?
-
What info do neurons encode in trains of action potentials (“spike trains”)?
-
How can we model “statically” encoded information?
-
Estimation/”decoding”
-
Signal conditioning – “spike sorting” (PCA, Expectation-Maximization)
-
How can we model/decode “dynamic” information? (filtering, Kalman, HMM)
-
Beyond spike trains (LFP, EEG, imaging)
Quick alternative formulation: Distance Maximization
Super useful for intuition and grandma level understanding
ELEC548 Lec21&22
By Shayok Dutta
