SpikeSift
  • Overview
  • Installation
  • User Guide
  • Example Usage
  • Performance
  • Implementation
  • API Reference
SpikeSift
  • Welcome to SpikeSift
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Welcome to SpikeSift

SpikeSift is a fast, drift-resilient spike sorting algorithm for high-density extracellular recordings. It runs in real time on a single CPU core and can be parallelized across segments for large-scale processing. Designed for speed, modularity, and robustness, it supports adaptive segmentation, segment merging, and progressive recording analysis with minimal tuning.

  • Overview
    • How Does SpikeSift Work?
    • Why Use SpikeSift?
    • What the Documentation Covers
  • Installation
    • Installing with pip
    • Installing from Source
    • Troubleshooting
    • Verifying Installation
  • User Guide
    • Running Spike Sorting
    • Working with the Sorted Output
    • Merging Results from Multiple Sorts
    • Comparing Clusters Across Recordings
    • Segment and Time Access
  • Example Usage
    • Performing Spike Sorting on a Single File
    • Parallel Spike Sorting Across Multiple Cores
    • Adding a Second File
    • Adding a Third File with Modified Scaling
    • Undoing or Refining a Merge
    • Matching Clusters Across Recordings
    • Tracking a Missing Cluster Across Segments
    • Tracking Transient Neurons in Long Recordings
    • Visualizing Sorted Spikes
  • Performance
    • Performance Highlights
    • Run a Quick Benchmark
    • Why Is SpikeSift So Fast?
  • Implementation
  • API Reference
    • Recording
    • SortedRecording
    • perform_spike_sorting()
    • merge_recordings()
    • map_clusters()

If you’re new to SpikeSift, start with the Overview, then follow the User Guide to run your first sort.

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© Copyright 2025, Vasileios Georgiadis.

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