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PCA fails to recover network parameters from simulated data
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Classroom Contents
Dimensionality Reduction for Matrix- and Tensor-Coded Data
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- 1 Intro
- 2 Modern experiments capture a large range of timescales in neural data
- 3 We apply standard tensor decomposition methods to extract these components
- 4 PCA fails to recover network parameters from simulated data
- 5 Seminal theorem (Kruskal, 1977) proves that linear independence is a sufficient condition for tensor decomposition identifiability
- 6 Application 11: How does a model network learn a sensory discrimination task?
- 7 Gain modulation is a compact and accurate model of the network activity over all trials
- 8 How does prefrontal cortex encode place, actions, and rewards during maze navigation?
- 9 TCA (gain modulation) is a very compact and accurate model for trial-to-trial variability
- 10 PCA components encode complex mixtures of task variables
