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Implementation of deep learning models in decoding fMRI/EEG data in a context of semantic processing

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Deep_learning_fMRI

Implementation of deep learning models in decoding fMRI data in a context of semantic processing

Goal:

  • To illustrate basic concepts of deep neural network models (DNN and CNN implementation so far)
  • To illustrate how to utilize deep neural network models to decode fMRI data
  • To illustrate principle ways of cross-validating the deep neural network models
  • To illustrate how upsupervised learning model is more straightforward to implement in deep neural network models than scit-kit learning implementation
  • To Propose a way to visualize the learned latent representations of the unsupervised models
  • To illustrate simulation experiments with CNN models
  • To illustrate representational similarity analysis
  • To illustrate the implementation of searchlight algorithm
  • To show how the literature has been using the deep neural network models in classification
  • To demonstrate how deep neural network models can decode conscious states

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Implementation of deep learning models in decoding fMRI/EEG data in a context of semantic processing

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