In quest of the missing neuron: Spike sorting based on dominant-sets clustering

Authors:
Dimitrios A. Adamos;Nikolaos A. Laskaris;Efstratios K. Kosmidis;George Theophilidis
Affiliations:
Laboratory of Animal Physiology, School of Biology, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece;Laboratory of Artificial Intelligence and Information Analysis, Department of Informatics, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece;Laboratory of Physiology, School of Medicine, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece;Laboratory of Animal Physiology, School of Biology, Aristotle University of Thessaloniki, 54 124 Thessaloniki, Greece
Venue:
Computer Methods and Programs in Biomedicine
Year:
2012

Citing 6
Cited 0

Unitary events in multiple single-neuron spiking activity: II. nonstationary data

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Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering

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Dominant Sets and Pairwise Clustering

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Performance evaluation of PCA-based spike sorting algorithms

Computer Methods and Programs in Biomedicine
An online spike detection and spike classification algorithm capable of instantaneous resolution of overlapping spikes

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De-noising by soft-thresholding

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Abstract

Spike sorting algorithms aim at decomposing complex extracellular signals to independent events from single neurons in the electrode's vicinity. The decision about the actual number of active neurons is still an open issue, with sparsely firing neurons and background activity the most influencing factors. We introduce a graph-theoretical algorithmic procedure that successfully resolves this issue. Dimensionality reduction coupled with a modern, efficient and progressively executable clustering routine proved to achieve higher performance standards than popular spike sorting methods. Our method is validated extensively using simulated data for different levels of SNR.