Pre-start
Announcements
- OH 4-6 today and Friday AM 2nd floor BRC Cafe
- Homework 2 submissions?
- 4-5 people notified me of late submissions. No one else mentioned anything but only 5 submissions as of 5pm Oct 17th. There isn't a hard deadline but things will pile up.
- What's up?
- Have I scared away more peoples? :/ :)
- I'm happy to help get this done or ask questions on Piazza and anyone can respond!
- 4-5 people notified me of late submissions. No one else mentioned anything but only 5 submissions as of 5pm Oct 17th. There isn't a hard deadline but things will pile up.
- Homework 3 is available with objectives listed and such. Lmk if there are any questions!
Neural Signal Processing & Machine Learning
ELEC/BIOE 548 | ELEC 483
Fall 2022
Episode 15: And so we begin to cluster...but first recap & data exploration
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!
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How can we measure neural activity?
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What info do neurons encode in trains of action potentials (“spike trains”)?
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How can we model “statically” encoded information?
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Estimation/”decoding”
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Signal conditioning – “spike sorting” (PCA, Expectation-Maximization)
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How can we model/decode “dynamic” information? (filtering, Kalman, HMM)
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Beyond spike trains (LFP, EEG, imaging)
The Journey
- Recognizing patterns and making machines learn!
- Chapter 4. Classification!
Quick recap
ELEC548 Lec15
By Shayok Dutta
