driada 0.7.2

Population and element-level analysis of neuronal computations

DRIADA

Dimensionality Reduction for Integrated Activity Data - A unified framework bridging single-neuron selectivity analysis with population-level dimensionality reduction for biological and artificial neural systems.

🎯 Vision

DRIADA creates a seamless bridge between understanding individual neurons and population-level neural dynamics. Our framework enables researchers to:

1. Identify which neurons encode specific variables (using INTENSE)
2. Extract collective latent variables from population activity
3. Connect single-cell selectivity to population manifolds
4. Interpret how neural populations represent information

The DRIADA Workflow

DRIADA uniquely combines single-neuron and population-level analyses in one framework. While traditional methods analyze neurons in isolation OR populations as a whole, DRIADA reveals how individual neural selectivity gives rise to collective representations.

Dimensionality reduction  ←  Population Activity  ←  Single Neurons  →  INTENSE
         ↓                                                                ↓
Latent Variables                                                 Individual Selectivity
         ↓                                                                ↓
          → → → → → → → → →  Integration Analysis ← ← ← ← ← ← ← ← ← ← ← ← ←
                                       ↓
         Connect single-cell selectivity to population-level variables

Overview

DRIADA provides a comprehensive toolkit for analyzing both individual neural selectivity and collective population dynamics:

• 🔍 Individual Analysis: Discover which neurons encode specific behavioral variables using information theory
• 🌐 Population Analysis: Extract latent variables and manifolds from neural population activity
• 🔗 Integrated Workflows: Connect single-cell properties to population-level representations
• 🧪 Validation Tools: Generate synthetic populations with known ground truth for algorithm testing

Key Capabilities

🧠 INTENSE Module - Single Neuron Analysis

• Detect both linear and nonlinear relationships using mutual information
• Rigorous two-stage statistical testing with multiple comparison correction
• Handle temporal delays between neural activity and behavior
• Disentangle mixed selectivity when neurons respond to multiple variables

📊 Population-Level Analysis - Collective Neural Dynamics

• Dimensionality Estimation: Measure intrinsic dimensionality of neural manifolds
  • Linear methods: PCA-based dimension, effective rank
  • Nonlinear methods: k-NN dimension, correlation dimension
• Dimensionality Reduction: Extract latent variables from population activity
  • Classical: PCA, Factor Analysis
  • Manifold learning: Isomap, UMAP, diffusion maps
  • Specialized neural methods (coming soon)
• Latent Variable Extraction: Recover behavioral variables from neural populations
  • Extract circular variables (e.g., head direction)
  • Reconstruct spatial maps from place cell activity
  • Identify task-relevant population subspaces

🔗 Integrated Analysis - Bridging Scales

• Map single-cell selectivity to population manifolds
• Understand how individual neurons contribute to collective representations
• Visualize relationships between neural selectivity and population structure

🧪 Synthetic Data Generation - Algorithm Validation

• Generate populations with known ground truth:
  • Head direction cells on circular manifolds
  • Place cells on 2D/3D spatial manifolds
  • Mixed populations with manifold + feature-selective neurons
• Test and validate analysis methods before applying to real data
• Benchmark different algorithms on controlled datasets

Perfect for:

• 🧠 Cognitive neuroscience: Identify task-relevant neural subspaces and their dynamics
• 🤖 AI interpretability: Understand representations in artificial neural networks
• 🔬 Systems neuroscience: Bridge cellular and population-level descriptions

Installation

# Basic installation
pip install driada

# With GPU support (recommended for large datasets)
pip install driada[gpu]

Quick Start

For complete code examples, tutorials, and API documentation, please visit the official documentation.

⚠️ WARNING: Pre-Release Version

DRIADA is currently in pre-release stage (v0.x.x) and will be finalized to v1.0 soon.

Until the stable v1.0 release:

• 📚 Documentation takes precedence over example code
• 🔧 Examples and notebooks may be incomplete or broken
• 🚧 API may undergo changes
• 📖 Please refer to the official documentation for the most up-to-date information

Documentation

📖 Official Documentation - Complete API reference, tutorials, and guides

Additional Resources

• INTENSE Module Guide - Neural selectivity analysis documentation
• GitHub Issues - Report bugs or request features

Requirements

• Python 3.8+
• NumPy, SciPy, scikit-learn
• numba (for performance optimization)
• matplotlib, seaborn (for visualization)
• See pyproject.toml for complete list

Installation from Source

git clone https://github.com/iabs-neuro/driada.git
cd driada
pip install -e .  # Editable installation

Contributing

We welcome contributions! Please see our Contributing Guidelines for details.

Development Setup

# Clone the repository
git clone https://github.com/iabs-neuro/driada.git
cd driada

# Create conda environment
conda create -n driada python=3.9
conda activate driada

# Install in development mode
pip install -e .[gpu]

# Run tests
pytest

Citation

If you use DRIADA in your research, please cite:

@software{driada2024,
  title = {DRIADA: Dimensionality Reduction for Integrated Activity Data},
  author = {Pospelov, Nikita and contributors},
  year = {2025},
  url = {https://github.com/iabs-neuro/driada}
}

Publications

DRIADA has been used in the following research:

Biological Neural Systems

• Sotskov et al. (2022) - Fast tuning dynamics of hippocampal place cells during free exploration
• Pospelov et al. (2024) - Effective dimensionality of hippocampal population activity correlates with behavior
• Bobyleva et al. (2025) - Multifractality of structural connectome eigenmodes

Artificial Neural Networks

• Kononov et al. (2024) - Hybrid computational dynamics in RNNs through reinforcement learning

Methodological Applications

• Pospelov et al. (2021) - Laplacian Eigenmaps for fMRI resting-state analysis

See PUBLICATIONS.md for the complete list with abstracts and details.

Support

• 📧 Email: [e-mail]
• 🐛 Issues: GitHub Issues
• 💬 Discussions: GitHub Discussions

License

This project is licensed under the MIT License - see the LICENSE file for details.

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Note: DRIADA is actively developed. We recommend using the latest stable release for production work.