The project focuses on analyzing neural activity data to classify neuron types (spiny and aspiny). It integrates unsupervised learning methods (PCA, Autoencoders) and supervised learning models (Logistic Regression, MLP) to build accurate classifiers that effectively analyze neurons' electrical responses.
pca-analysis supervised-learning logistic-regression data-compression mlp perceptron gradient-descent unsupervised-learning neuron autoencoders stochastic-gradient-descent neuronal-network roc-auc mlp-networks neural-classification high-dimensional-neural-datasets classifier-evaluation cortical-neurons 2d-and-3d-visualizations
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Updated
Jan 9, 2025 - Jupyter Notebook