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Braincoder documentation
Braincoder documentation
  • Tutorial
    • Lesson 1: Encoding models
    • Lesson 2: Linear encoding models
    • Lesson 3: Building the likelihood (by fitting the covariance matrix)
    • Lesson 4: From neural responses to stimulus features
    • Lesson 5: Decoding noisy neural data
    • Lesson 6: Decoding two-dimensional stimulus spaces
    • Decode the marginal probability distribution
    • Decode the conditional probability distribution
    • The complex plane
    • Lesson 7: Fitting PRF models to visual space
  • Examples
    • Examples
      • Fit the residual covariance matrix
      • Stimulus decoding using stimulus mask
      • Linear encoding model
      • Create a simple Gaussian Prf encoding model
      • Invert encoding model
      • Decoding of stimuli from neural data
      • Recontruct a 2D visual stimulus from real fMRI data
      • Fit a 2D PRF model
      • Fisher information to estimate precision of encoding parameters across stimulus space
      • Different flavors of visual population receptive field models
      • Decoded stimulus: Gaussian model
      • Decoded stimulus: DN model
      • Decoding 2D stimuli from neural data
  • General bibliography
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