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Neural Signal Processing & Machine Learning

ELEC/BIOE 548 | ELEC 483

Fall 2022

Episode 12: Finish Poisson-ing & Begin Classifying!

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”

  • How can we model/decode “dynamic” information? (filtering, Kalman, HMM)

  • Signal conditioning – “spike sorting” (PCA, Expectation-Maximization)

  • Beyond spike trains (LFP, EEG, imaging)

The Journey

  • Recognizing patterns and making machines learn!
    • Chapter 4. Classification!

Examples of classification

Examples of classification

Examples of classification

Examples of classification

EXPERIENCE/DECISIONS!

Bayesian?

Story time!

What would you do??

Poisson IPad Notes

Classifying Neural Responses

Classifying Neural Responses

Classifying Neural Responses

Motor System Classification

Motor System Classification

Motor System Classification

Motor System Classification

Motor System Classification

Motor System Classification

Motor System Classification

Monkey Reach Task

ELEC548 Lec12&13

By Shayok Dutta

ELEC548 Lec12&13

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