# SpikeDeep-Classifier: A deep-learning based fully automatic offline spike sorting algorithm

arXiv: https://arxiv.org/abs/1912.10749 · HTML: https://mlsanity.com/paper/1912.10749/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2019.

## Built on by (most cited first, 5 of 5)

- [ConTraNet: A single end-to-end hybrid network for EEG-based and EMG-based human machine interfaces](https://mlsanity.com/paper/2206.10677/) (2022)
- [Adaptive SpikeDeep-Classifier: Self-organizing and self-supervised machine learning algorithm for online spike sorting](https://mlsanity.com/paper/2304.01355/) (2023)
- [Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review](https://mlsanity.com/paper/2409.17516/) (2024)
- [An Adaptive Contrastive Learning Model for Spike Sorting](https://mlsanity.com/paper/2205.11914/) (2022)
- [SSI-GAN: Semi-Supervised Swin-Inspired Generative Adversarial Networks for Neuronal Spike Classification](https://mlsanity.com/paper/2601.00189/) (2026)

## Abstract (arXiv)

Objective. Recent advancements in electrode designs and micro-fabrication technology has allowed existence of microelectrode arrays with hundreds of channels for single-cell recordings. In such electrophysiological recordings, each implanted micro-electrode can record the activities of more than one neuron in its vicinity. Recording the activities of multiple neurons may also be referred to as multiple unit activity. However, for any further analysis, the main goal is to isolate the activity of each recorded neuron and thus called single-unit activity. This process may also be referred to as spike sorting or spike classification. Recent approaches to extract SUA are time consuming, mainly due to the requirement of human intervention at various stages of spike sorting pipeline. Lack of standardization is another drawback of the current available approaches. Therefore, in this study we proposed a standard spike sorter: SpikeDeep-Classifier, a fully automatic spike sorting algorithm. Approach. We proposed a novel spike sorting pipeline, based on a set of supervised and unsupervised learning algorithms. We used supervised, deep learning-based algorithms for extracting meaningful channels and removing background activities (noise) from the extracted channels. We also showed that the process of clustering becomes straight-forward, once the noise/artifact is completely removed from the data. Therefore, in the next stage, we applied a simple clustering algorithm (K-mean) with predefined maximum number of clusters. Lastly, we used a similarity-based criterion to keep distinct clusters and merge similar-looking clusters. Main results. We evaluated our algorithm on a dataset collected from two different species (humans and non-human primates (NHPs)) without any retraining. We also validated our algorithm on two publicly available labeled datasets.