DualSort: online spike sorting with a running neural network | JoVE Visualize

Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Spike sorting, the process of identifying and separating neuronal action potentials, is a critical yet challenging step in brain activity analysis.
Existing neural network (NN) approaches often focus on individual components of the spike sorting pipeline, requiring complex architectures.
There is a need for efficient, low-complexity methods for real-time spike sorting with reduced manual intervention.

Purpose of the Study:

To introduce DualSort, a simple NN combined with post-processing for efficient and real-time spike sorting.
To demonstrate that high performance in spike detection and sorting can be achieved without complex NN architectures, even under high noise.
To reduce the need for extensive manual labeling through data augmentation techniques.

Main Methods:

DualSort, a simple neural network (NN), was trained and evaluated using synthetic and experimental single-channel extracellular recordings.
The NN detects and categorizes spike waveforms in unison by classifying spikes iteratively across the signal.
A downstream post-processing algorithm refines the NN output into precise spike trains, enhancing overall system robustness.

Main Results:

DualSort successfully detected, distinguished, and separated different neuronal spike waveforms from background noise.
The integrated post-processing significantly improved the model's performance and robustness.
DualSort demonstrates competitive performance against state-of-the-art methods that target specific sub-problems.

Conclusions:

Simple neural networks, like DualSort, coupled with post-processing, are sufficient for high-performance spike sorting, challenging the need for complex architectures.
The framework enables reduced manual labeling through data augmentation and can operate autonomously with unsupervised pseudo-labeling.
DualSort's low complexity facilitates efficient real-time processing on basic hardware and shows potential for analyzing other biosignals like EEG.