DeepSpike

Deep-Spike: Foundation Model-based Pipeline for Large-Scale Spike Sorting of Neural Activity

Overview

Spike sorting of high-resolution neural recordings is essential for understanding brain activity, but it remains challenging when multiple units are recorded due to their overlapping spike timing, low signal-to-noise ratios and overlapping clusters. Here, we introduce DeepSpike, a self-supervised deep learning model that automates spike sorting and overcomes key limitations of conventional spike sorting methods. DeepSpike is pretrained on large-scale unlabelled spiking events obtained from electrophysiological data as a general foundation model, enabling it to generalize to new recordings without dataset-specific retraining. DeepSpike uses a self-supervised autoencoder to learn robust low-dimensional spike embeddings that facilitate accurate clustering and effective noise filtering. The model is trained on a new, large-scale dataset consisting of $255M$ spiking events (SpikeVault-255M) derived from real in vivo recordings of about $4560$ minutes duration. The dataset consists of $15M$ ground truth spikes that are manually verified by an expert user. DeepSpike outperformed state-of-the-art spike sorting algorithms in both accuracy and robustness in our experiments on SpikeVault-255M, and two public benchmark datasets. Our results demonstrate that DeepSpike provides a scalable and generalizable solution for large-scale neural spike sorting. SpikeVault-255M dataset and the pretrained DeepSpike model are provided for further use and development.

Features

• End-to-end spike sorting workflow
• Deep learning-based feature extraction (Autoencoder, VAE)
• Multiple clustering methods (GMM, DPGMM, HDBSCAN, KMeans)
• Integration with SpikeInterface for standardized spike sorting and evaluation
• Visualization tools for embeddings and clustering results
• Support for large public datasets

Repository Structure

• clustering.py: Clustering algorithms and utilities
• dataset.py: Dataset loading and preprocessing
• models.py: Deep learning models (VAE)
• preprocess.py: Data preprocessing functions
• utils.py: Utility functions
• models/: Pretrained model weights
• notebooks/: Example Jupyter notebooks and analysis pipelines
• tables/: SpikeVault255M and Public dataset metrics and recording details

Getting Started

1. Clone the repository

   git clone https://github.com/HughYau/DeepSpike.git
   cd DeepSpike

2. Install dependencies Make sure you have Python 3.8+ and install the required packages:

   pip install -r requirements.txt

3. Run example notebooks Open notebooks/deep_spike_guideline.ipynb for a step-by-step demonstration.