Welcome to DRIADA’s documentation!
DRIADA (Dimensionality Reduction for Integrated Activity Data Analysis) is a comprehensive Python library for analyzing neural population activity through the lens of both single-neuron selectivity and population-level dimensionality reduction.
Getting Started
API Reference
Additional Information
Key features
INTENSE Analysis: Information-theoretic selectivity analysis for single neurons
Dimensionality Reduction: Comprehensive suite including PCA, UMAP, Isomap, autoencoders, and more
Network Analysis: Graph-based analysis of neural connectivity and dynamics
Recurrence Analysis: Delay embedding, recurrence plots, RQA, visibility graphs, and ordinal partition networks for nonlinear dynamics
Integration Framework: Seamlessly connect single-neuron selectivity with population manifolds
RSA: Representational Similarity Analysis for comparing neural representations
Information Theory: Entropy and mutual information estimators with advanced corrections
Synthetic Data: Generate controlled datasets with known ground truth
Visualization Tools: Rich plotting utilities for neural data exploration