An Accurate and Robust Method for Spike Sorting Based on Convolutional Neural Networks

Abstract

In the fields of neuroscience and biomedical signal processing, spike sorting is a crucial step to extract the information of single neurons from extracellular recordings. In this paper, we propose a novel deep learning approach based on one-dimensional convolutional neural networks (1D-CNNs) to implement accurate and robust spike sorting. The results of the simulated data demonstrated that the clustering accuracy in most datasets was greater than 99%, despite the multiple levels of noise and various degrees of overlapped spikes. Moreover, the proposed method performed significantly better than the state-of-the-art method named “WMsorting” and a deep-learning-based multilayer perceptron (MLP) model. In addition, the experimental data recorded from the primary visual cortex of a macaque monkey were used to evaluate the proposed method in a practical application. It was shown that the method could successfully isolate most spikes of different neurons (ranging from two to five) by training the 1D-CNN model with a small number of manually labeled spikes. Considering the above, the deep learning method proposed in this paper is of great advantage for spike sorting with high accuracy and strong robustness. It lays the foundation for application in more challenging works, such as distinguishing overlapped spikes and the simultaneous sorting of multichannel recordings.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings

“…Therefore, to achieve storage reduction, neural networks may be shrunk to three layers of artificial neurons, where additional attention elements complete the network (Bernert and Yvert, 2019 ). Sequentially constructed algorithms, such as those building upon multiple basic dense layers (Mahallati et al, 2019 ; Yeganegi et al, 2020 ) and convolutional (Li et al, 2020b ) and recurrent layers (Rácz et al, 2020 ) require an expansive repository, although by weights' and activation functions' binarization, complexity may be cut back (Valencia and Alimohammad, 2021 ), or parallelization by graphical processing units may take place (Tam and Yang, 2018 ). These layers may be constructed in different ways, mainly in order to mitigate or abandon the need for hand-labeled neural data throughout training: autoencoders (Weiss, 2019 ; Radmanesh et al, 2021 ; Rokai et al, 2021 ) or networks generated by adversarial (Wu et al, 2019 ; Ciecierski, 2020 ) or reinforcement learning paradigms (Salman et al, 2018 ; Moghaddasi et al, 2020 ) have successfully clustered features originating from noisiest datasets.…”

Spike sorting: new trends and challenges of the era of high-density probes

“…The last few years have witnessed a boom in the efforts of using deep learning approaches to tackle the spike sorting problem [60,20,62,71,43,92,91,81,89,47]. Deep learning methods, in fact, have proven so powerful and accurate in many complicated applications, ranging from image classification to natural language processing.…”

“…Li et. al [47] developed a similar approach focused on recordings from single channels. However, instead of splitting the detection and sorting phase, the used a singe 1D CNN architecture to directly detect and sort spikes in the input recording.…”

A Robust Spike Sorting Method based on the Joint Optimization of Linear Discrimination Analysis and Density Peaks

“…In addition, the spike overlapping, electrode drift and explosive discharge under real conditions are simulated. To date, ‫ݏݑ݈ܿ_݁ݒܽݓ‬ has been used by many spike sorting algorithms for evaluating sorting performance [19][20][21][22]24].…”

“…Other methods like Principal Components Analysis (PCA) [13,16] and Discrete Wavelet Transform (DWT) [17–19] have certain robustness to noise. In recent years, deep learning methods have been proposed, such as the 1D-CNNs [20] and the Autoencoder (AE) [21]. However, the deep learning methods are limited in practical use because of their high computational complexity and high demands on the training set.…”