Automated spike sorting algorithmbased on Laplacian eigenmaps and<i>k</i>-means clustering

Chah, Ehsan; Hok, Vincent; Della-Chiesa, Andrea; Miller, John J. H.; O’Mara, Shane M.; Reilly, Richard B.

doi:10.1088/1741-2560/8/1/016006

Cited by 59 publications

(64 citation statements)

References 45 publications

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“…However, one could also find in the literature approaches such as paramagnetic clustering (Quiroga et al, 2004), mean-shift clustering (Swindale and Spacek, 2014) or even k -means clustering (Atiya, 1992; Chah et al, 2011). Another interesting approach is to consider the most consensual clustering across an ensemble of k -means solutions (Fournier et al, 2016).…”

Section: The Challenge Posed By Large-scale Multi-electrode Recordmentioning

confidence: 99%

Recent progress in multi-electrode spike sorting methods

Lefebvre

Yger

Marre

2016

Journal of Physiology-Paris

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In recent years, arrays of extracellular electrodes have been developed and manufactured to record simultaneously from hundreds of electrodes packed with a high density. These recordings should allow neuroscientists to reconstruct the individual activity of the neurons spiking in the vicinity of these electrodes, with the help of signal processing algorithms. Algorithms need to solve a source separation problem, also known as spike sorting. However, these new devices challenge the classical way to do spike sorting. Here we review different methods that have been developed to sort spikes from these large-scale recordings. We describe the common properties of these algorithms, as well as their main differences. Finally, we outline the issues that remain to be solved by future spike sorting algorithms.

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Section: The Challenge Posed By Large-scale Multi-electrode Recordmentioning

confidence: 99%

Recent progress in multi-electrode spike sorting methods

Lefebvre

Yger

Marre

2016

Journal of Physiology-Paris

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“…In the recent years, with the development of artificial neural networks, [14] classifies the low ratio signals via artificial neural networks whereas the accuracy is not very well. Fuzzy c-means and k-means do a good job in [15,16]. These clustering algorithms are based on distance, nevertheless, their initial cluster centers are generated randomly.…”

Section: Introductionmentioning

confidence: 99%

Spike sorting based on PCA and improved fuzzy c-means

Yu¹,

Zhao²,

Liu³

et al. 2015

Advances in Computer Science Research

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“…Wood and Black applied nonparametric Bayesian alternative to spike of classification [10]. Chah E proposed Laplacian eige-nmaps and k-means clustering algorithm for automated spike classification [11].…”

Section: Introductionmentioning

confidence: 99%

Tetrode Spike Detection Method Based on Quaternion Principle Component Feature Extraction

Zhao¹,

Wang²,

Tan³

2014

Proceedings of the 2014 International Conference on Mechatronics, Electronic, Industrial and Control Engineering

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Abstract-Multi-unit recording of tetrode has been used in spike detections for many years, but traditional feature extraction methods of spikes sorting based on analyzing correlations of single channel data don't consider relations of each channel data. A new feature extraction method based on quaternion principle component analysis (QPCA) algorithm used for sorting the tetrode spikes trains is presented. A quaternion vector formed of fourdimensional numbers can replace a set of tetrode spikes data. Firstly, the four channels of spikes were achieved using dual threshold detection method. We used the modulus values of the vectors grouped by 4 channels corresponding data to constitute the new features vectors of spikes through QPCA algorithm. The features vectors contain correlations of 4 channels. Our method fully fuses the information of tetrode data. Thus it has higher clustering accuracy than traditional feature extraction methods.

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Automated spike sorting algorithmbased on Laplacian eigenmaps andk-means clustering

Cited by 59 publications

References 45 publications

Recent progress in multi-electrode spike sorting methods

Recent progress in multi-electrode spike sorting methods

Spike sorting based on PCA and improved fuzzy c-means

Tetrode Spike Detection Method Based on Quaternion Principle Component Feature Extraction

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