SpikeLab

Python library for spike train analysis of neural electrophysiology data.

SpikeLab provides a complete toolkit for loading, analyzing, and exporting neuronal spike train data from multi-electrode array (MEA) electrophysiology experiments.

Key features:

• Spike and rate data structures – SpikeData and RateData classes for representing raw spike trains and instantaneous firing rates, with built-in analysis methods (ISI, firing rate, burst detection, and more).

• Event-aligned slicing – Extract trial-aligned windows of spike or rate data around stimulus events, stored as SpikeSliceStack and RateSliceStack objects for easy averaging and visualization.

• Pairwise comparison matrices – Compute unit-by-unit similarity or distance matrices with PairwiseCompMatrix and stack them across conditions with PairwiseCompMatrixStack.

• Flexible I/O – Load data from pickle, NWB, Neo, and custom formats; export to CSV, pickle, or HDF5 workspaces; optionally read from Amazon S3.

• MCP server – Programmatic access to all core analysis via the Model Context Protocol, enabling integration with LLM-based analysis tools.

Note

SpikeLab’s analyses assume that spike times are provided in milliseconds. Make sure your data uses this convention before passing it to analysis functions.

Contents

• Getting Started
  • Installation
  • Quick Start
• Guides
  • Loading Data
  • Spike Sorting and Curation
  • Spike Analysis
  • Instantaneous Firing Rates
  • Event-Aligned Analysis
  • Pairwise Analysis
  • Shuffled Data
  • GPLVM Analysis
  • Exporting Data
  • Workspace Persistence
  • MCP Server
  • Parallel Computing
  • Batch Jobs
• API Reference
  • Core Data Structures
  • Utilities
  • Data I/O
  • Persistence
  • Spike Sorting and Curation
  • Batch Jobs
  • Server

Indices and Tables

• Index

• Module Index

• Search Page