CATSort 0.1.2

A collision-aware template matching spike sorter.

CATSort

CATSort (Collision-Aware Template-matching Sort) is a robust spike sorter designed to handle overlapping spikes (collisions) with high precision using a specific collision-handling stage before clustering followed by template matching.

Key Features

• Collision Handling: Automatically identifies and flags collided spikes using multi-criterion feature analysis (amplitude, width, energy).
• Template Matching: Robust spike extraction using template-based matching (including 'wobble' for now).
• Flexible Schemes: Choose between an adaptive threshold optimization or an original fixed MAD (Median Absolute Deviation) multiplier scheme.
• SpikeInterface Integration: Fully compatible with the SpikeInterface ecosystem.

Installation

You can install catsort via pip:

pip install catsort

Or from source:

git clone https://github.com/lucasbeziers/CATSort.git
cd CATSort
pip install -e .

Quick Start

import spikeinterface.extractors as se
from catsort import run_catsort

# Load your recording
recording = se.read_binary("path_to_data.dat", sampling_frequency=30000, num_channels=384, dtype="int16")

# Run CATSort
sorting = run_catsort(recording)

# The result is a SpikeInterface Sorting object
print(sorting)

Parameters

CATSort offers several parameters to fine-tune its behavior:

• scheme: 'original' or 'adaptive'.
  • 'adaptive' uses temporal collisions to optimize thresholds.
  • 'original' uses fixed MAD multipliers.
• mad_multiplier_amplitude, mad_multiplier_width, mad_multiplier_energy: (Default: 7.0, 10.0, 15.0) Used when scheme='original'.
• detect_threshold: Spike detection threshold in standard deviations (Default: 5).

License

MIT License. See LICENSE for details.