Overview

SpikeSift is a high-performance spike sorting algorithm designed for high-density extracellular recordings. It delivers state-of-the-art accuracy while running in real time on a single CPU core.

How Does SpikeSift Work?

SpikeSift extracts and tracks individual neuron activity from raw extracellular recordings. It includes:

  • Filtering and segmenting the signal based on drift-aware heuristics

  • Iteratively detecting and clustering spikes within each segment

  • Merging matching clusters across segments to produce globally aligned spike trains

The result is a drift-corrected, neuron-by-neuron reconstruction of spiking activity across time.

Why Use SpikeSift?

SpikeSift is built for speed, robustness, and clean integration into any workflow:

  • Extremely fast — sorts thousands of channels in real time on a single CPU core

  • Drift-resilient — handles both gradual and abrupt electrode drift

  • Clean and non-intrusive — no data copying, no file modifications, no clutter

  • Modular — sort in parallel, split or merge segments, track transients

  • Drop-in ready — works out of the box on most datasets

  • Session-aware — merge across files, append sessions, or sort progressively

  • Reliable on short recordings — maintains accuracy even with limited data

These features make SpikeSift ideal for real-time pipelines, high-throughput labs, and large-scale sorting tasks — even on resource-constrained systems.

What the Documentation Covers

The rest of this documentation includes:

For a quick start, see the User Guide. To explore practical workflows, head to Example Usage.

Note

SpikeSift is under active development and continues to improve in accuracy and flexibility. For more details or citations, see the upcoming preprint: