EnsemblesGUI documentation

Graphic User Interface to perform a variety of neuronal ensembles identification’s methods using calcium or electrical recordings.

Note

This project is under current development.

This GUI currently incorporates five different algorithms:

• SVD based method: Velazquez-Contreras, R., Carrillo-Reid, L. (2025). Identification of Neuronal Ensembles from Similarity Maps Using Singular Value Decomposition. In: Carrillo-Reid, L. (eds) Identification, Characterization, and Manipulation of Neuronal Ensembles. Neuromethods, vol 215. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-4208-5_5

• PCA based method: Herzog et al. (2021) “Scalable and accurate automated method for neuronal ensemble detection in spiking neural networks. https://pubmed.ncbi.nlm.nih.gov/34329314/ Rubén Herzog Dec 2021

• ICA based method: Lopes-dos-Santos V, Ribeiro S, Tort AB (2013) Detecting cell assemblies in large neuronal populations. J Neurosci Methods 220(2):149-66. 10.1016/j.jneumeth.2013.04.010

• Xsembles2P method: Pérez-Ortega, J., Akrouh, A. & Yuste, R. (2024). Stimulus encoding by specific inactivation of cortical neurons. Nat Commun 15, 3192. doi: 10.1038/s41467-024-47515-x

• Similarity Graph Clustering method: L. Avitan et al. “Spontaneous Activity in the Zebrafish Tectum Reorganizes over Development and Is Influenced by Visual Experience”. Curr. Biol. 27 (2017). DOI: 10.1016/j.cub.2017.06.056

Contents

Contents:

• Installation
  • System requirements
  • Installation of the python environment
  • Installation of the MATLAB engine for Python
  • Run the GUI
  • References
• Usage
  • 1. Execute EnsemblesGUI
  • 2. Load Your Data
  • 3. Run Analyses
  • 4. Visualize Results
  • 5. Compare Results
  • 6. Evaluate Algorithm Performance
  • 7. Save Results
  • Additional Tips
• Code functions
  • MainWindow
  • WorkerRunnable
  • WorkerSignals
• Acknowledgments