https://github.com/iabs-neuro/driada
Dimensionality Reduction for Integrated Activity Data Analysis
https://github.com/iabs-neuro/driada
computational-neuroscience dimensionality-reduction information-theory network-analysis neural-coding neural-computation neuroscience time-series-analysis
Last synced: 3 months ago
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Dimensionality Reduction for Integrated Activity Data Analysis
- Host: GitHub
- URL: https://github.com/iabs-neuro/driada
- Owner: iabs-neuro
- License: mit
- Created: 2023-04-07T10:39:49.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2026-03-31T13:30:38.000Z (3 months ago)
- Last Synced: 2026-03-31T15:28:00.290Z (3 months ago)
- Topics: computational-neuroscience, dimensionality-reduction, information-theory, network-analysis, neural-coding, neural-computation, neuroscience, time-series-analysis
- Language: Python
- Homepage:
- Size: 113 MB
- Stars: 8
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Citation: CITATION.cff
- Authors: AUTHORS.yaml
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README
# DRIADA
**D**imensionality **R**eduction for **I**ntegrated **A**ctivity **D**ata **A**nalysis — a Python framework for cross-scale analysis of neural population activity.
[](https://www.python.org/downloads/)
[](https://pypi.org/project/driada/)
[](https://pypi.org/project/driada/)
[](LICENSE)
[](https://github.com/iabs-neuro/driada/actions/workflows/tests.yml)
[](https://codecov.io/gh/iabs-neuro/driada)
[](https://driada.readthedocs.io/en/latest/)
[](https://github.com/iabs-neuro/driada/actions/workflows/tests.yml)
## Integrating analysis scales
Neuroscience describes brain activity through three complementary paradigms: **single-neuron selectivity** (what does each cell encode?), **population dynamics** (what is the collective geometry?), and **networks** (how are units connected?). These are not competing theories — they are complementary lenses on the same data. The same neuron can be viewed simultaneously as a feature detector, a dimension of a population manifold, and a node in a functional circuit.
Yet in practice, each paradigm has its own tools, its own data formats, its own packages. If you want to ask a question that crosses paradigm boundaries — *does selectivity predict network membership? does filtering by tuning improve manifold structure? how do individual neurons contribute to population coding? do temporal dynamics of single neurons reveal population-level organisation?* — you have to build a custom pipeline from incompatible parts.
DRIADA provides a **shared data model** where all three paradigms operate on the same objects. Results from one domain flow directly into the others:
Three bidirectional bridges connect the paradigms:
- **Neurons ↔ Populations.** Selective neurons filter the population matrix for dimensionality reduction; embedding coordinates feed back into neuron-level importance estimation, revealing which cells drive each manifold dimension.
- **Neurons ↔ Networks.** Pairwise MI significance testing builds functional connectivity from neural activity; per-neuron recurrence graphs, visibility graphs, and ordinal partition networks yield functional networks grounded in temporal dynamics rather than correlations.
- **Populations ↔ Networks.** Graph-based dimensionality reduction (Isomap, diffusion maps) returns proximity graphs that inherit the full network toolkit — spectral decomposition, community detection, entropy — without format conversion. RSA provides a complementary route from population representations to network structure.
DRIADA's strength is integrating these scales — crossing them should be a function call, not a weekend of data wrangling.
## 🔬 Key Capabilities
- 🧠 **[INTENSE](https://driada.readthedocs.io/en/latest/api/intense.html)** — detect neuron-feature selectivity via mutual information with rigorous two-stage statistical testing, delay optimization, and mixed selectivity disentanglement
- 📊 **[Dimensionality Reduction](https://driada.readthedocs.io/en/latest/api/dim_reduction.html)** — linear (PCA), graph-based (Isomap, diffusion maps, LLE), neighbor-embedding (UMAP, t-SNE), and neural network-based (autoencoders) with manifold quality metrics
- 📐 **[Dimensionality Estimation](https://driada.readthedocs.io/en/latest/api/dimensionality.html)** — PCA-based, effective rank, k-NN, correlation, and geodesic dimension
- 🔗 **[Integration](https://driada.readthedocs.io/en/latest/api/integration.html)** — map single-cell selectivity onto population manifolds and embedding components
- 🌐 **[Network Analysis](https://driada.readthedocs.io/en/latest/api/network.html)** — general-purpose graph analysis (spectral, entropy, communities) for connectomes, functional networks, or dimensionality reduction proximity graphs
- 🔄 **[Recurrence Analysis](https://driada.readthedocs.io/en/latest/api/recurrence.html)** — delay embedding, recurrence plots, RQA, visibility graphs, ordinal partition networks for nonlinear dynamics and population module recovery
- 📏 **[RSA](https://driada.readthedocs.io/en/latest/api/rsa.html)** — representational dissimilarity matrices, cross-region and cross-session comparisons
- 🧪 **[Synthetic Data](https://driada.readthedocs.io/en/latest/api/experiment.html)** — generate populations with known ground truth for validation
- ⚡ **[Neuron](https://driada.readthedocs.io/en/latest/api/experiment.html)** — calcium kinetics optimization (rise/decay fitting), event detection, and signal quality metrics
## 🚀 Tutorials
Interactive notebooks — click a badge to open in Google Colab (no setup required):
| | Notebook | Topics |
|---|---|---|
| [](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/00_driada_overview.ipynb) | **DRIADA overview** | `Experiment` objects, feature types, quick tour of INTENSE, dimensionality reduction, networks |
| [](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/01_data_loading_and_neurons.ipynb) | **Neuron analysis** | Spike reconstruction, kinetics optimization, quality metrics, surrogates |
| [](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/02_selectivity_detection_intense.ipynb) | **Selectivity detection (INTENSE)** | Mutual information, two-stage testing, optimal delays, mixed selectivity |
| [](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/03_population_geometry_dr.ipynb) | **Population geometry & DR** | PCA, UMAP, Isomap, autoencoders, manifold quality metrics, dimensionality estimation |
| [](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/04_network_analysis.ipynb) | **Network analysis** | Cell-cell significance, spectral analysis, communities, graph entropy |
| [](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/05_advanced_capabilities.ipynb) | **Advanced capabilities** | Embedding selectivity, leave-one-out importance, RSA, RNN analysis |
| [](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/06_recurrence_analysis.ipynb) | **Recurrence analysis** | Delay embedding, recurrence plots, RQA, visibility & ordinal graphs, population module recovery |
All notebooks generate synthetic data internally — no external files needed. See the [examples reference](https://driada.readthedocs.io/en/latest/examples.html) for 25+ standalone scripts covering additional use cases.
## Data
DRIADA is designed for **calcium imaging** data but works with any neural activity represented as a `(n_units, n_frames)` array — RNN activations, firing rates, LFP channels, or anything else. Behavioral variables are 1D or multi-component arrays of the same length. Variable types (continuous, discrete, circular, multivariate) are auto-detected and preprocessed appropriately.
**Input workflow:** load your arrays into a Python dict and call `load_exp_from_aligned_data`:
```python
from driada.experiment import load_exp_from_aligned_data
from driada.intense import (compute_cell_feat_significance,
compute_cell_cell_significance, compute_embedding_selectivity)
from driada.rsa import compute_experiment_rdm
from driada.network import Network
from driada.integration import get_functional_organization
import scipy.sparse as sp
data = {
'calcium': calcium_array, # (n_neurons, n_frames) — or 'activations', 'rates', etc.
'speed': speed_array, # (n_frames,) continuous variable
'position': position_array, # (2, n_frames) multi-component variable
'head_direction': hd_array, # auto-detected as circular
'trial_type': trial_type_array, # auto-detected as discrete
}
exp = load_exp_from_aligned_data('MyLab', {'name': 'session1'}, data,
static_features={'fps': 30.0})
feat_stats, feat_sig, *_ = compute_cell_feat_significance(exp) # neuron-feature selectivity
cell_sim, cell_sig, *_ = compute_cell_cell_significance(exp) # functional connectivity
net = Network(adj=sp.csr_matrix(cell_sig), preprocessing='giant_cc') # network analysis
embedding = exp.create_embedding('umap', n_components=2) # dimensionality reduction
rdm, labels = compute_experiment_rdm(exp, items='trial_type') # representational similarity
emb_res = compute_embedding_selectivity(exp, ['umap']) # which neurons encode which components
org = get_functional_organization(exp, 'umap',
intense_results=emb_res['umap']['intense_results']) # selectivity-to-embedding bridge
```
You can also load directly from `.npz` files via `load_experiment()`. See the [RNN analysis tutorial](https://colab.research.google.com/github/iabs-neuro/driada/blob/main/notebooks/05_advanced_capabilities.ipynb) for a non-calcium example.
## Installation
```bash
pip install driada
# Optional extras
pip install driada[gpu] # autoencoders, torch-based methods
pip install driada[mvu] # MVU dimensionality reduction (cvxpy)
pip install driada[all] # everything
# From source
git clone https://github.com/iabs-neuro/driada.git
cd driada
pip install -e ".[dev]"
```
DRIADA pulls in ~30 dependencies (numpy, scipy, scikit-learn, numba, joblib, etc.). See [pyproject.toml](pyproject.toml) for the full list.
## Documentation
📖 **[driada.readthedocs.io](https://driada.readthedocs.io)** — API reference, installation guide, and quickstart
📋 **[Changelog](CHANGELOG.md)** — release history and migration notes
## Contributing
We welcome contributions! Please see [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
```bash
git clone https://github.com/iabs-neuro/driada.git
cd driada
pip install -e ".[dev]"
pytest
```
## Citation
If you use DRIADA in your research, please cite:
```bibtex
@software{driada2026,
title = {DRIADA: Dimensionality Reduction for Integrated Activity Data Analysis},
author = {Pospelov, Nikita and contributors},
year = {2026},
url = {https://github.com/iabs-neuro/driada}
}
```
## Publications
DRIADA has been used in the following research:
### Biological neural systems
- **[Sotskov et al. (2022)](https://doi.org/10.3390/ijms23020638)** — Fast tuning dynamics of hippocampal place cells during free exploration
- **[Pospelov et al. (2024)](https://doi.org/10.1109/DCNA63495.2024.10718588)** — Effective dimensionality of hippocampal population activity correlates with behavior
- **[Bobyleva et al. (2025)](https://doi.org/10.1162/netn_a_00439)** — Multifractality of structural connectome eigenmodes
### Artificial neural networks
- **[Kononov et al. (2025)](https://arxiv.org/abs/2510.11162)** — Hybrid computational dynamics in RNNs through reinforcement learning
### Methodological applications
- **[Pospelov et al. (2021)](https://doi.org/10.1016/j.ynirp.2021.100035)** — Laplacian Eigenmaps for fMRI resting-state analysis
**See [PUBLICATIONS.md](PUBLICATIONS.md) for the complete list with abstracts and details.**
## Support
- 📧 **Email**: pospelov.na14@physics.msu.ru
- 🐛 **Issues**: [GitHub Issues](https://github.com/iabs-neuro/driada/issues)
- 💬 **Discussions**: [GitHub Discussions](https://github.com/iabs-neuro/driada/discussions)
## License
This project is licensed under the MIT License — see the [LICENSE](LICENSE) file for details.