scaling-analysis 0.1.0

Analysis of the dimensionality of neuronal population dynamics as a function of neuron number

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.