speed-spike-sort 0.1.0

High-performance framework for neural spike sorting of electrophysiological data

SpeedSort: High-Performance Neural Spike Sorting Framework

Overview

SpeedSort is a high-performance framework for neural spike sorting, designed to handle various electrophysiology data formats with minimal configuration requirements. It supports GPU acceleration, adaptive dimensionality reduction, and integrated quality metrics for automated unit verification.

Features

• Universal data format handling with automatic detection
• GPU-accelerated preprocessing and clustering when available
• Adaptive dimensionality reduction and feature selection
• Parallelized processing for multi-channel recordings
• Integrated quality metrics with automated unit verification
• Minimal configuration with intelligent defaults

Installation

To run this project, you need to have Python 3.6 or higher installed. You can clone the repository and install the required dependencies using pip.

1. Clone the repository:

   git clone https://github.com/NileshArnaiya/Speed-Spike-Sort.git
   cd speed-spike-sort
   

2. Install the required packages:

   pip install -r requirements.txt
   

   Make sure to install pynwb, numpy, scipy, pandas, and scikit-learn. If you want to use GPU acceleration, install torch and cupy as well.

Usage

To run the spike sorting process, you can use the provided spike-sort-run.py script. Here's how to execute it:

python feuapp/spike-sort-run.py --data <path_to_your_data_file> --sampling-rate <sampling_rate> --data-format <data_format>

Command Line Arguments

• --data: Path to the input data file (required).
• --sampling-rate: Sampling rate in Hz (default: 30000).
• --data-format: Data format (options: numpy, binary, neo, nwb, mda, auto, default: auto).
• --use-gpu: Use GPU acceleration if available (optional).
• --jobs: Number of parallel jobs (default: number of CPU cores - 1).
• --filter-low: Low cutoff frequency in Hz (default: 300).
• --filter-high: High cutoff frequency in Hz (default: 6000).
• --detection-method: Spike detection method (default: threshold_dynamic).
• --max-clusters: Maximum number of clusters to detect (default: 50).
• --min-cluster-size: Minimum number of spikes per cluster (default: 30).
• --output: Path to save results (default: spike_sorting_results.pkl).

Example

python spike-sort-run.py --data test_data/random_numpy_data.npy --sampling-rate 30000 --max-clusters 5 --data-format nwb

Contributing

We welcome contributions to SpeedSort! If you would like to contribute, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix:

   git checkout -b feature/my-feature
   

3. Make your changes and commit them:

   git commit -m "Add some feature"
   

4. Push your changes to your fork:

   git push origin feature/my-feature
   

5. Create a pull request describing your changes.

License

This project is licensed under the GNU Public License. See the LICENSE file for details.

Acknowledgments

• NumPy
• SciPy
• Pandas
• PyTorch
• CuPy
• scikit-learn
• MNE-Python