zstoebs / neural_dim_reduction

Project on classification dimensionality reduction using neural data

Dimensionality Reduction on Neural Data

Exploring dimensionality reduction with classification of neural data.

The original dataset and article Sofroniew, Nicholas James et al. “Neural coding in barrel cortex during whisker-guided locomotion.” can be found on the author's GitHub repo.

Contents

• PCA applied to a waveform to cell type problem
• An autoencoder applied to a waveform to cell type problem

Other references:

Cunningham, J., Yu, B. Dimensionality reduction for large-scale neural recordings. Nat Neurosci 17, 1500–1509 (2014). https://doi.org/10.1038/nn.3776

Paninski L, Cunningham JP. Neural data science: accelerating the experiment-analysis- theory cycle in large-scale neuroscience. Curr Opin Neurobiol. 2018 Jun;50:232-241. doi: 10.1016/j.conb.2018.04.007. PMID: 29738986.

Wu, Tong et al. “Deep Compressive Autoencoder for Action Potential Compression in Large-Scale Neural Recording.” Journal of Neural Engineering 15.6 (2018): n. pag. Journal of Neural Engineering. Web.

Ladjal, Saïd, Alasdair Newson, and Chi Hieu Pham. “A PCA-like Autoencoder.” arXiv 2 Apr. 2019: n. pag. Print.

Scree and cumulative explained variance plots

Matplotlib 3D scatter plot

Keras autoencoder guide

Hyperparameter grid search for Keras:

• https://machinelearningmastery.com/grid-search-hyperparameters-deep-learning-models-python-keras/
• https://stackoverflow.com/questions/49823192/autoencoder-gridsearch-hyperparameter-tuning-keras
• https://towardsdatascience.com/autoencoders-for-the-compression-of-stock-market-data-28e8c1a2da3e