scaling_analysis
scaling_analysis
enables estimation of the reliable dimensionality of neuronal population dynamics and its scaling as a function of the number of sampled neurons, as described by Manley et al. Neuron 2024.
The most important functions include:
svca.run_SVCA_partition samples a given number of neurons and performs shared variance component analysis (SVCA) using PopulationCoding.dimred.SVCA.
predict.predict_from_behavior performs SVCA on a sampling of a specified number of neurons and then predicts the neural SVCs from behavioral variables.
Note that the analysis modules each contain a command line interface (CLI) which is described in the main()
function within each module in the API.
Usage
pip install scaling_analysis
Check out the demos for examples of the analyses described in Manley et al. 2024.
Example datasets are freely available at https://doi.org/10.5281/zenodo.10403684.
Check out the full API in the documentation.
Citation
If you use this package, please cite the paper:
Manley, J., Lu, S., Barber, K., Demas, J., Kim, H., Meyer, D., Martínez Traub, F., & Vaziri, A. (2024). Simultaneous, cortex-wide dynamics of up to 1 million neurons reveal unbounded scaling of dimensionality with neuron number. Neuron. https://doi.org/10.1016/j.neuron.2024.02.011.
- Demo: 3x5mm Right Hemisphere FOV
- Demo: 5x6mm Bi-hemispheric FOV
- Demo: 1.2x1.2mm FOV
- scaling_analysis package
- Submodules
- scaling_analysis.experiment module
- scaling_analysis.pca module
- scaling_analysis.plotting module
- scaling_analysis.predict module
- scaling_analysis.predict_history module
- scaling_analysis.session_permutation module
- scaling_analysis.spatial module
- scaling_analysis.svca module
- scaling_analysis.temporal module
- scaling_analysis.utils module
- scaling_analysis.visual module
- Module contents