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

Name		Name	Last commit message	Last commit date
Latest commit History 83 Commits
.gitignore		.gitignore
10_1_searchlight_representational_similarity_analysis,_bonus_decoding.ipynb		10_1_searchlight_representational_similarity_analysis,_bonus_decoding.ipynb
10_1_searchlight_representational_similarity_analysis.ipynb		10_1_searchlight_representational_similarity_analysis.ipynb
10_2_searchlight_RSA_word_embedding.ipynb		10_2_searchlight_RSA_word_embedding.ipynb
10_3_searchlight_RSA_computer_vision_VGGNet.ipynb		10_3_searchlight_RSA_computer_vision_VGGNet.ipynb
11_1_analyze_semantic_stimuli_before_experiment_using_word_embedding_models.ipynb		11_1_analyze_semantic_stimuli_before_experiment_using_word_embedding_models.ipynb
1_perceptron.ipynb		1_perceptron.ipynb
2_full_connected_neural_network.ipynb		2_full_connected_neural_network.ipynb
3_1_some_concepts_of_CNN.ipynb		3_1_some_concepts_of_CNN.ipynb
3_image_classification_with_CNN,_transfer_learning,_cat_vs_dog.ipynb		3_image_classification_with_CNN,_transfer_learning,_cat_vs_dog.ipynb
4_1_Autoencoder_in_fMRI.ipynb		4_1_Autoencoder_in_fMRI.ipynb
4_2_Autoencoder_in_fMRI_continue.ipynb		4_2_Autoencoder_in_fMRI_continue.ipynb
4_2_Autoencoder_in_fMRI_continue_(VAE).ipynb		4_2_Autoencoder_in_fMRI_continue_(VAE).ipynb
4_3_Autoencoder_in_fMRI_continue_convolutional_neural_network_with_transfer_learning.ipynb		4_3_Autoencoder_in_fMRI_continue_convolutional_neural_network_with_transfer_learning.ipynb
4_4_XGB_implementation.ipynb		4_4_XGB_implementation.ipynb
4_5_simple_multi_perceptron_keras_implementation.ipynb		4_5_simple_multi_perceptron_keras_implementation.ipynb
5_1_How_to_represent_experiment_stimuli_via_word2vec_models.ipynb		5_1_How_to_represent_experiment_stimuli_via_word2vec_models.ipynb
5_2_a_simple_encoding_with_word2vec.ipynb		5_2_a_simple_encoding_with_word2vec.ipynb
5_3_demonstrate_the_DNN_works.ipynb		5_3_demonstrate_the_DNN_works.ipynb
5_4_back_tracking_gradients_and_derive_an_encoding_model_from_the_decoding_model.ipynb		5_4_back_tracking_gradients_and_derive_an_encoding_model_from_the_decoding_model.ipynb
5_5_Extract_computer_vision_representations_of_pokemons_.ipynb		5_5_Extract_computer_vision_representations_of_pokemons_.ipynb
5_6_Extract_CV_representation_of_experiment_stimuli_words.ipynb		5_6_Extract_CV_representation_of_experiment_stimuli_words.ipynb
5_7_extract_features_from_sentences.ipynb		5_7_extract_features_from_sentences.ipynb
5_8_extract_features_from_scene_imags.ipynb		5_8_extract_features_from_scene_imags.ipynb
5_9_extract_features_from_socialcon_image_name_fast_text_model.ipynb		5_9_extract_features_from_socialcon_image_name_fast_text_model.ipynb
6_1_C_RNN_decoding_models_of_EEG_data.ipynb		6_1_C_RNN_decoding_models_of_EEG_data.ipynb
7_2_simulation_of_statistical_learning_of_triplets.ipynb		7_2_simulation_of_statistical_learning_of_triplets.ipynb
8_1_get_feature_embeddings.ipynb		8_1_get_feature_embeddings.ipynb
8_2_extract_representations_from_words.ipynb		8_2_extract_representations_from_words.ipynb
8_3_extract_representations_from_images.ipynb		8_3_extract_representations_from_images.ipynb
9_1_1_simulate_a_Gabor_Patch_identification_experiment.ipynb		9_1_1_simulate_a_Gabor_Patch_identification_experiment.ipynb
9_1_2_simulate_a_Gabor_Patch_identification_experiment,_circular_grating_mask.ipynb		9_1_2_simulate_a_Gabor_Patch_identification_experiment,_circular_grating_mask.ipynb
Cole_et_al_2017_CNN_3D_fMRI.ipynb		Cole_et_al_2017_CNN_3D_fMRI.ipynb
Copy_of_model_architecture_testing.ipynb		Copy_of_model_architecture_testing.ipynb
Guclu_and_van_Gerven_2015_ventral_representation.ipynb		Guclu_and_van_Gerven_2015_ventral_representation.ipynb
Han_et_al_2019_VAE_and_fMRI.ipynb		Han_et_al_2019_VAE_and_fMRI.ipynb
LICENSE		LICENSE
README.md		README.md

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