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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!

  • 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)

Probabilistic PCA (PPCA)

But why tho?

ELEC548 Lec23

By Shayok Dutta

ELEC548 Lec23

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