pfbalan/deep_learning_fmri

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

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 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