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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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Functional architecture of the FSPS™ programme. The main menu (1) allows the user to initiate new recordings (2), visualize raw data or results (3) and to get fast access to any stage of the data processing (4 and 5). All other modules of the programme are loop-structured and prompt the user to execute a procedure when necessary. Dynamic links between modules and storage of critical parameters during SVD of data matrix and unsupervised FCM allows the program to solve the invariance problem during PCA, automatically select features to be used in online fuzzy classification, choose the best settings for clustering (new or previously calculated for each recording site) and remove noise. Elements included in the dotted block are crucial parameters obtained during multivariate analysis and unsupervised FCM, which are stored externally from the programme for use during fuzzy classification. These are updated when necessary. Output classes (namely, labelled groups of PCs), together with participating elements, thus represent different single neurons of specific action potential waveform, and then indicate their location in the raw signal. Elements marked with dashed line (VI 1 and VI 2) are two separated Virtual Instruments (VI) working simultaneously either on one computer with multi-core processor architecture or two different single-processor computers connected to a LAN. Solid blocks and lines inside VI 1 element – parts of the programme involved in the online procedures; dotted blocks and lines – parts that run once to get the prototype, before starting online classification.
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Figure 1: Functional architecture of the FSPS™ programme. The main menu (1) allows the user to initiate new recordings (2), visualize raw data or results (3) and to get fast access to any stage of the data processing (4 and 5). All other modules of the programme are loop-structured and prompt the user to execute a procedure when necessary. Dynamic links between modules and storage of critical parameters during SVD of data matrix and unsupervised FCM allows the program to solve the invariance problem during PCA, automatically select features to be used in online fuzzy classification, choose the best settings for clustering (new or previously calculated for each recording site) and remove noise. Elements included in the dotted block are crucial parameters obtained during multivariate analysis and unsupervised FCM, which are stored externally from the programme for use during fuzzy classification. These are updated when necessary. Output classes (namely, labelled groups of PCs), together with participating elements, thus represent different single neurons of specific action potential waveform, and then indicate their location in the raw signal. Elements marked with dashed line (VI 1 and VI 2) are two separated Virtual Instruments (VI) working simultaneously either on one computer with multi-core processor architecture or two different single-processor computers connected to a LAN. Solid blocks and lines inside VI 1 element – parts of the programme involved in the online procedures; dotted blocks and lines – parts that run once to get the prototype, before starting online classification.

Mentions: In this article, we develop and present a spike sorting software called FSPS (Fuzzy SPike Sorting) which is designed to overcome these limitations. The FSPS software (whose architecture is sketched in Figure 1) increases the robustness and speed of PCA-based spike sorting means of several steps. First, it carefully preprocesses the data to improve the alignment of spike shapes. Second, it uses a Partial Single Value Decomposition (PSVD) preprocessing technique to extract PCs[13]. This technique is computationally efficient because it exploits previously computed Single Value Decomposition (SVD) for the further dynamic low-rank approximation of new coming waveforms by means of series of simple matrix operations on the output eigenvectors and at the same times reduces the noise in the components to be sorted[14,15]. Third, the algorithm uses the information obtained during SVD to classify the neuronal waveforms by means of FCM clustering[16-19]. The unsupervised nature of FCM and its ability to detect clusters of different shapes makes it particularly useful for online sorting because of its robustness to non-stationary recordings, responsible of the smeared clusters in the high-dimensional feature space. Fourth, to control the accuracy of neuron isolation, the software provides several objective isolation quality measures, including the Lratio measure which allows a comparison of cross-laboratory clustering[20].


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

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

Functional architecture of the FSPS™ programme. The main menu (1) allows the user to initiate new recordings (2), visualize raw data or results (3) and to get fast access to any stage of the data processing (4 and 5). All other modules of the programme are loop-structured and prompt the user to execute a procedure when necessary. Dynamic links between modules and storage of critical parameters during SVD of data matrix and unsupervised FCM allows the program to solve the invariance problem during PCA, automatically select features to be used in online fuzzy classification, choose the best settings for clustering (new or previously calculated for each recording site) and remove noise. Elements included in the dotted block are crucial parameters obtained during multivariate analysis and unsupervised FCM, which are stored externally from the programme for use during fuzzy classification. These are updated when necessary. Output classes (namely, labelled groups of PCs), together with participating elements, thus represent different single neurons of specific action potential waveform, and then indicate their location in the raw signal. Elements marked with dashed line (VI 1 and VI 2) are two separated Virtual Instruments (VI) working simultaneously either on one computer with multi-core processor architecture or two different single-processor computers connected to a LAN. Solid blocks and lines inside VI 1 element – parts of the programme involved in the online procedures; dotted blocks and lines – parts that run once to get the prototype, before starting online classification.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Functional architecture of the FSPS™ programme. The main menu (1) allows the user to initiate new recordings (2), visualize raw data or results (3) and to get fast access to any stage of the data processing (4 and 5). All other modules of the programme are loop-structured and prompt the user to execute a procedure when necessary. Dynamic links between modules and storage of critical parameters during SVD of data matrix and unsupervised FCM allows the program to solve the invariance problem during PCA, automatically select features to be used in online fuzzy classification, choose the best settings for clustering (new or previously calculated for each recording site) and remove noise. Elements included in the dotted block are crucial parameters obtained during multivariate analysis and unsupervised FCM, which are stored externally from the programme for use during fuzzy classification. These are updated when necessary. Output classes (namely, labelled groups of PCs), together with participating elements, thus represent different single neurons of specific action potential waveform, and then indicate their location in the raw signal. Elements marked with dashed line (VI 1 and VI 2) are two separated Virtual Instruments (VI) working simultaneously either on one computer with multi-core processor architecture or two different single-processor computers connected to a LAN. Solid blocks and lines inside VI 1 element – parts of the programme involved in the online procedures; dotted blocks and lines – parts that run once to get the prototype, before starting online classification.
Mentions: In this article, we develop and present a spike sorting software called FSPS (Fuzzy SPike Sorting) which is designed to overcome these limitations. The FSPS software (whose architecture is sketched in Figure 1) increases the robustness and speed of PCA-based spike sorting means of several steps. First, it carefully preprocesses the data to improve the alignment of spike shapes. Second, it uses a Partial Single Value Decomposition (PSVD) preprocessing technique to extract PCs[13]. This technique is computationally efficient because it exploits previously computed Single Value Decomposition (SVD) for the further dynamic low-rank approximation of new coming waveforms by means of series of simple matrix operations on the output eigenvectors and at the same times reduces the noise in the components to be sorted[14,15]. Third, the algorithm uses the information obtained during SVD to classify the neuronal waveforms by means of FCM clustering[16-19]. The unsupervised nature of FCM and its ability to detect clusters of different shapes makes it particularly useful for online sorting because of its robustness to non-stationary recordings, responsible of the smeared clusters in the high-dimensional feature space. Fourth, to control the accuracy of neuron isolation, the software provides several objective isolation quality measures, including the Lratio measure which allows a comparison of cross-laboratory clustering[20].

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