How do we extract, measure, and model spike trains from neuronal recordings? In this hands-on workshop, participants will explore electrophysiological data—both real and simulated—to investigate the firing patterns of single neurons and neural populations. Participants will study descriptive techniques like raster plots, PSTHs, and cross-correlograms, alongside statistical modeling approaches such as poisson-based generative models and advanced coherence-based analyses. Practical work is grounded both in Python’s core scientific stack (NumPy, SciPy, Pandas, Scikit-learn) and neuroscience-specific Python libraries (Neo, Elephant), with small-group exercises linking methods to real-world experiments in mice. Participants will also gain experience with the full spike analysis pipeline, including filtering, peak detection, PCA-based clustering, and spike sorting, both from first principles using Scikit-Learn and with the powerful pipelines provided per Python’s SpikeInterface.

Learning and participation

Topics

  • Electrophysiology-based Spike Extraction: Filtering, peak detection, and PCA-based clustering, with Scipy-Signal, Scikt-Learn and SpikeInterface
  • Spike Train Metrics: Raster plots, PSTH, CV, cross-correlograms, and receptive field mapping, using Numpy, Matplotlib, and Pandas
  • Poisson-Based Statistical Modeling: Generative models and coherence analysis (UEA & SPADE), using Scipy-Stats and Elephant

Intended participants

  • Researchers and students from all universities are welcome.
  • Participants of all educational backgrounds are welcome.

Useful resources for preparation

Certification

Students who attend at least 75% of the course will receive a participation certificate by email at the end of the course.