Pre-start
Honest and high quality extension requests will always be granted.
Announcements
- Office hours tmrw morning 10-12
- Slight scheduling update: 11/15 class will just be recorded. No guest lecturer.
- Final project type up just give me the week to finish writing it up and sharing data/paper links
- Quick Check-in: Any feedback? Rest of the classes will be mathy and you implement the math on neural data but practically it's the same thing for any data.
Neural Signal Processing & Machine Learning
ELEC/BIOE 548 | ELEC 483
Fall 2022
Episode 20: Gaussian Mixture Models!
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)
Gaussian Mixture Models
Gaussian Mixture Models
After first E Step
After first M Step
Gaussian Mixture Models
Gaussian Mixture Models
Gaussian Mixture Models
Gaussian Mixture Models
Gaussian Mixture Models
Gaussian Mixture Models
Gaussian Mixture Models
ELEC548 Lec20
By Shayok Dutta
