A Novel and Simple Spike Sorting Implementation

Abstract

Monitoring the activity of multiple, individual neurons that fire spikes in the vicinity of an electrode, namely perform a Spike Sorting (SS) procedure, comprises one of the most important tools for contemporary neuroscience in order to reverse-engineer the brain. As recording electrodes' technology rabidly evolves by integrating thousands of electrodes in a confined spatial setting, the algorithms that are used to monitor individual neurons from recorded signals have to become even more reliable and computationally efficient. In this work, we propose a novel framework of the SS approach in which a single-step processing of the raw (unfiltered) extracellular signal is sufficient for both the detection and sorting of the activity of individual neurons. Despite its simplicity, the proposed approach exhibits comparable performance with state-of-the-art approaches, especially for spike detection in noisy signals, and paves the way for a new family of SS algorithms with the potential for multi-recording, fast, on-chip implementations.

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

“…Whether dense-packed structures, for instance pyramidal cells of the hippocampus, are under investigation, plus population activity as local field potential brings sufficient information, individual electrodes constitute an ideal choice (Rutishauser et al, 2006 ). On the other hand, it is also evident that single unit activity can hardly be relieved by a single electrode recording because of the abundance of independently firing nearby neurons, without any knowledge of their spatial coordinates (Petrantonakis and Poirazi, 2017 ).…”

“…Analyzing spike trains and spatiotemporal properties of extracellular AP waveforms provides us precious evidence of a cell's functional profile and morphology, including dendritic tree architecture, surrounding environment, and relative position of the recording site (Chaure et al, 2018 ; Rodriguez-Collado and Rueda, 2021 ; Soleymankhani and Shalchyan, 2021 ) and sheds light on the meticulously orchestrated functioning of neural networks (Leibig et al, 2016 ; Luan et al, 2018 ). Besides providing insight into brain activity at the highest temporal resolution currently available (Rey et al, 2015 ; Wouters et al, 2021 ), facilitating the “reverse-engineering” of the brain (Petrantonakis and Poirazi, 2017 ), extracellular APs are eagerly sourced in the development of brain-machine interfaces, too (Hammad et al, 2016 ).…”

Spike detection and spike sorting with a hidden Markov model improves offline decoding of motor cortical recordings

“…T = 2 in their paper), with the standard deviation found using the median absolute value method. As per their method, the maximum values of the two D w in a window around detection time act as features for online spike sorting [35,36]. In their study [35], the authors use a k-means classifier for offline sorting.…”

“…As per their method, the maximum values of the two D w in a window around detection time act as features for online spike sorting [35,36]. In their study [35], the authors use a k-means classifier for offline sorting. We compute centroids (means of features D 5 and D 15 ) from the training data and classify spikes by picking the centroid with smallest distance to the feature values of the spike.…”

“…Commonly-used spike detection methods in the BCI field, voltage thresholding and voltage absolute value thresholding (in hardware: [8], in software: [9][10][11][12]), as well as alternative methods, such as those involving derivatives (online software: [13]), the non-linear energy operator (hardware: [14,15], software: [8,9,11,[16][17][18][19][20][21][22]), wavelet analysis (hardware: [23,24], software: [9,19,[25][26][27][28][29]), Hilbert transform (hardware: [30], software: [31,32]), empirical mode decomposition (software: [31]), fractal dimension (software: [16,33]), peak-to-trough height (software: [34]), and Euclidian distance between successive windows (software: [35,36]), all compute a scalar feature from the recent voltage samples and flag extreme values of this feature as detections. However, it is possible that, due to noise corruption, a real spike may not trigger the detector.…”