Pre-start
Announcements
- End of classes schedule
- Final Homework
- Final Project
- Happy Thanksgiving!
- General questions!
Neural Signal Processing & Machine Learning
ELEC/BIOE 548 | ELEC 483
Fall 2022
Episodes 23 The end is near: Finishing PPCA & Factor Analysis
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...but smoothly this time!
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)
Probabilistic PCA (PPCA)
But why tho?
