Abstract
The most widely used spike-sorting algorithms are semiautomatic in practice, requiring manual tuning of the automatic solution to achieve good performance. In this work, we propose a new fully automatic spike-sorting algorithm that can capture multiple clusters of different sizes and densities. In addition, we introduce an improved feature selection method, by using a variable number of wavelet coefficients, based on the degree of non-Gaussianity of their distributions. We evaluated the performance of the proposed algorithm with real and simulated data. With real data from single-channel recordings, in ~95% of the cases the new algorithm replicated, in an unsupervised way, the solutions obtained by expert sorters, who manually optimized the solution of a previous semiautomatic algorithm. This was done while maintaining a low number of false positives. With simulated data from single-channel and tetrode recordings, the new algorithm was able to correctly detect many more neurons compared with previous implementations and also compared with recently introduced algorithms, while significantly reducing the number of false positives. In addition, the proposed algorithm showed good performance when tested with real tetrode recordings.NEW & NOTEWORTHY We propose a new fully automatic spike-sorting algorithm, including several steps that allow the selection of multiple clusters of different sizes and densities. Moreover, it defines the dimensionality of the feature space in an unsupervised way. We evaluated the performance of the algorithm with real and simulated data, from both single-channel and tetrode recordings. The proposed algorithm was able to outperform manual sorting from experts and other recent unsupervised algorithms.
Highlights
Extracellular recordings of single-neuron activity are done by placing electrodes in brain tissue
On the basis of the different data partitions generated by the superparamagnetic clustering (SPC) algorithm, which are shown in the temperature plot in Fig. 1A, bottom, left, an expert user selected four clusters (3 single units and 1 multiunit) at different temperatures
We evaluated the number of hits with the former and new Wave_clus implementations using a variable number of wavelet coefficients
Summary
Extracellular recordings of single-neuron activity are done by placing electrodes in brain tissue. The importance of spike sorting is stressed by the fact that nearby neurons recorded from the same electrode can respond to completely different things, and, it is crucial to know which spike corresponds to which neuron. This is the case, for example, in the human and the rat hippocampus, where nearby neurons fire to unrelated concepts in the first case (De Falco et al 2016; Rey et al 2015a) and to distant place fields in the latter (Redish et al 2001)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
