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Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

Oliynyk A, Bonifazzi C, Montani F, Fadiga L - BMC Neurosci (2012)

Bottom Line: The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise.This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity.This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

View Article: PubMed Central - HTML - PubMed

Affiliation: Section of Human Physiology, Department of Biomedical Sciences and Advanced Therapies, Faculty of Medicine, University of Ferrara, Via Fossato di Mortara 17/19, 44121, Ferrara, Italy. lynnry@unife.it

ABSTRACT

Background: Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue.

Results: Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms.

Conclusions: This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

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Validation of waveforms in determined Classes with corresponding histograms and rasters. This Figure shows how the outcome of classification of datasets A and B, respectively, can be backwards applied to the raw signals to build rasters and histograms describing individual neurons’ response during the experimental task. Notes: 0 – all spikes, Unclassified; 1 – Class 1, 2 – Class 2; 3 – Class 3; 4 – Class 4.
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Figure 12: Validation of waveforms in determined Classes with corresponding histograms and rasters. This Figure shows how the outcome of classification of datasets A and B, respectively, can be backwards applied to the raw signals to build rasters and histograms describing individual neurons’ response during the experimental task. Notes: 0 – all spikes, Unclassified; 1 – Class 1, 2 – Class 2; 3 – Class 3; 4 – Class 4.

Mentions: To prove that the outcome of PCs clustering analysis/classification of the datasets is successful as well as obtained classes are assigned same single units, the results were backwards applied to the raw signals to build rasters and histograms describing individual neuron response (Figure 12). The firing properties were consistent across the two datasets suggesting that the units classified in dataset B are the same as those discovered in dataset A, and so the FSPS software can accurately track neurons despite non-stationarities in the data. Besides, the robustness of our method is demonstrated by comparing clustering results of two types of high-amplitude discharges, isolated as Class 2 and Class 3 in four PCs features space, and having specific reciprocal electrophysiological behaviour (see PSTHs and rasters in Figure 12).


Automatic online spike sorting with singular value decomposition and fuzzy C-mean clustering.

Oliynyk A, Bonifazzi C, Montani F, Fadiga L - BMC Neurosci (2012)

Validation of waveforms in determined Classes with corresponding histograms and rasters. This Figure shows how the outcome of classification of datasets A and B, respectively, can be backwards applied to the raw signals to build rasters and histograms describing individual neurons’ response during the experimental task. Notes: 0 – all spikes, Unclassified; 1 – Class 1, 2 – Class 2; 3 – Class 3; 4 – Class 4.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3473300&req=5

Figure 12: Validation of waveforms in determined Classes with corresponding histograms and rasters. This Figure shows how the outcome of classification of datasets A and B, respectively, can be backwards applied to the raw signals to build rasters and histograms describing individual neurons’ response during the experimental task. Notes: 0 – all spikes, Unclassified; 1 – Class 1, 2 – Class 2; 3 – Class 3; 4 – Class 4.
Mentions: To prove that the outcome of PCs clustering analysis/classification of the datasets is successful as well as obtained classes are assigned same single units, the results were backwards applied to the raw signals to build rasters and histograms describing individual neuron response (Figure 12). The firing properties were consistent across the two datasets suggesting that the units classified in dataset B are the same as those discovered in dataset A, and so the FSPS software can accurately track neurons despite non-stationarities in the data. Besides, the robustness of our method is demonstrated by comparing clustering results of two types of high-amplitude discharges, isolated as Class 2 and Class 3 in four PCs features space, and having specific reciprocal electrophysiological behaviour (see PSTHs and rasters in Figure 12).

Bottom Line: The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise.This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity.This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

View Article: PubMed Central - HTML - PubMed

Affiliation: Section of Human Physiology, Department of Biomedical Sciences and Advanced Therapies, Faculty of Medicine, University of Ferrara, Via Fossato di Mortara 17/19, 44121, Ferrara, Italy. lynnry@unife.it

ABSTRACT

Background: Understanding how neurons contribute to perception, motor functions and cognition requires the reliable detection of spiking activity of individual neurons during a number of different experimental conditions. An important problem in computational neuroscience is thus to develop algorithms to automatically detect and sort the spiking activity of individual neurons from extracellular recordings. While many algorithms for spike sorting exist, the problem of accurate and fast online sorting still remains a challenging issue.

Results: Here we present a novel software tool, called FSPS (Fuzzy SPike Sorting), which is designed to optimize: (i) fast and accurate detection, (ii) offline sorting and (iii) online classification of neuronal spikes with very limited or human intervention. The method is based on a combination of Singular Value Decomposition for fast and highly accurate pre-processing of spike shapes, unsupervised Fuzzy C-mean, high-resolution alignment of extracted spike waveforms, optimal selection of the number of features to retain, automatic identification the number of clusters, and quantitative quality assessment of resulting clusters independent on their size. After being trained on a short testing data stream, the method can reliably perform supervised online classification and monitoring of single neuron activity. The generalized procedure has been implemented in our FSPS spike sorting software (available free for non-commercial academic applications at the address: http://www.spikesorting.com) using LabVIEW (National Instruments, USA). We evaluated the performance of our algorithm both on benchmark simulated datasets with different levels of background noise and on real extracellular recordings from premotor cortex of Macaque monkeys. The results of these tests showed an excellent accuracy in discriminating low-amplitude and overlapping spikes under strong background noise. The performance of our method is competitive with respect to other robust spike sorting algorithms.

Conclusions: This new software provides neuroscience laboratories with a new tool for fast and robust online classification of single neuron activity. This feature could become crucial in situations when online spike detection from multiple electrodes is paramount, such as in human clinical recordings or in brain-computer interfaces.

Show MeSH
Related in: MedlinePlus