  Objective. Spike sorting, a critical step in neural data processing, aims to
classify spiking events from single electrode recordings based on different
waveforms. This study aims to develop a novel online spike sorter, NeuSort,
using neuromorphic models, with the ability to adaptively adjust to changes in
neural signals, including waveform deformations and the appearance of new
neurons. Approach. NeuSort leverages a neuromorphic model to emulate
template-matching processes. This model incorporates plasticity learning
mechanisms inspired by biological neural systems, facilitating real-time
adjustments to online parameters. Results. Experimental findings demonstrate
NeuSort's ability to track neuron activities amidst waveform deformations and
identify new neurons in real-time. NeuSort excels in handling non-stationary
neural signals, significantly enhancing its applicability for long-term spike
sorting tasks. Moreover, its implementation on neuromorphic chips guarantees
ultra-low energy consumption during computation. Significance. NeuSort caters
to the demand for real-time spike sorting in brain-machine interfaces through a
neuromorphic approach. Its unsupervised, automated spike sorting process makes
it a plug-and-play solution for online spike sorting.
