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Pipeline Consisting of LSTM + Variational and Transformer Based Autoencoders + PCA/UMAP (Parameterized and Non-Parameterized) For Generating Low-Dim Manifold Representation of V1 Neural Activity

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ankushgpta2/Vision2DeepManifold

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dPCA
dPCA
 
 
data
data
 
 
models
models
 
 
modules
modules
 
 
README.md
README.md
 
 
handle_inputs.py
handle_inputs.py
 
 
main.py
main.py
 
 
plot_outputs.py
plot_outputs.py
 
 
plot_outputs2.py
plot_outputs2.py
 
 
shallow_ml_main.py
shallow_ml_main.py
 
 
shallow_ml_plotting.py
shallow_ml_plotting.py
 
 

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LSTM_Autoencoder_And_Transformer_Latent_Model

Comprehensive Pipeline Consisting of LSTM + Variational and Transformer Based Autoencoders + PCA/UMAP (Parameterized and Non-Parameterized) For Generating Low-Dimensional Manifold Representation of V1 Neural Activity

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Pipeline Consisting of LSTM + Variational and Transformer Based Autoencoders + PCA/UMAP (Parameterized and Non-Parameterized) For Generating Low-Dim Manifold Representation of V1 Neural Activity

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machine-learning deep-neural-networks deep-learning dimensionality-reduction manifold manifold-learning latent-variables neuromorphic-computing latent-space renormalization-group scaling-exponent

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