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  1. Article: Improved space breakdown method - A robust clustering technique for spike sorting.

    Ardelean, Eugen-Richard / Ichim, Ana-Maria / Dînşoreanu, Mihaela / Mureşan, Raul Cristian

    Frontiers in computational neuroscience

    2023  Volume 17, Page(s) 1019637

    Abstract: Space Breakdown Method (SBM) is a clustering algorithm that was developed specifically for low-dimensional neuronal spike sorting. Cluster overlap and imbalance are common characteristics of neuronal data that produce difficulties for clustering methods. ...

    Abstract Space Breakdown Method (SBM) is a clustering algorithm that was developed specifically for low-dimensional neuronal spike sorting. Cluster overlap and imbalance are common characteristics of neuronal data that produce difficulties for clustering methods. SBM is able to identify overlapping clusters through its design of cluster centre identification and the expansion of these centres. SBM's approach is to divide the distribution of values of each feature into chunks of equal size. In each of these chunks, the number of points is counted and based on this number the centres of clusters are found and expanded. SBM has been shown to be a contender for other well-known clustering algorithms especially for the particular case of two dimensions while being too computationally expensive for high-dimensional data. Here, we present two main improvements to the original algorithm in order to increase its ability to deal with high-dimensional data while preserving its performance: the initial array structure was substituted with a graph structure and the number of partitions has been made feature-dependent, denominating this improved version as the Improved Space Breakdown Method (ISBM). In addition, we propose a clustering validation metric that does not punish overclustering and such obtains more suitable evaluations of clustering for spike sorting. Extracellular data recorded from the brain is unlabelled, therefore we have chosen simulated neural data, to which we have the ground truth, to evaluate more accurately the performance. Evaluations conducted on synthetic data indicate that the proposed improvements reduce the space and time complexity of the original algorithm, while simultaneously leading to an increased performance on neural data when compared with other state-of-the-art algorithms.
    Code available at: https://github.com/ArdeleanRichard/Space-Breakdown-Method.
    Language English
    Publishing date 2023-02-20
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452964-3
    ISSN 1662-5188
    ISSN 1662-5188
    DOI 10.3389/fncom.2023.1019637
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Time-Frequency Representations of Brain Oscillations: Which One Is Better?

    Bârzan, Harald / Ichim, Ana-Maria / Moca, Vasile Vlad / Mureşan, Raul Cristian

    Frontiers in neuroinformatics

    2022  Volume 16, Page(s) 871904

    Abstract: Brain oscillations are thought to subserve important functions by organizing the dynamical landscape of neural circuits. The expression of such oscillations in neural signals is usually evaluated using time-frequency representations (TFR), which resolve ... ...

    Abstract Brain oscillations are thought to subserve important functions by organizing the dynamical landscape of neural circuits. The expression of such oscillations in neural signals is usually evaluated using time-frequency representations (TFR), which resolve oscillatory processes in both time and frequency. While a vast number of methods exist to compute TFRs, there is often no objective criterion to decide which one is better. In feature-rich data, such as that recorded from the brain, sources of noise and unrelated processes abound and contaminate results. The impact of these distractor sources is especially problematic, such that TFRs that are more robust to contaminants are expected to provide more useful representations. In addition, the minutiae of the techniques themselves impart better or worse time and frequency resolutions, which also influence the usefulness of the TFRs. Here, we introduce a methodology to evaluate the "quality" of TFRs of neural signals by quantifying how much information they retain about the experimental condition during visual stimulation and recognition tasks, in mice and humans, respectively. We used machine learning to discriminate between various experimental conditions based on TFRs computed with different methods. We found that various methods provide more or less informative TFRs depending on the characteristics of the data. In general, however, more advanced techniques, such as the superlet transform, seem to provide better results for complex time-frequency landscapes, such as those extracted from electroencephalography signals. Finally, we introduce a method based on feature perturbation that is able to quantify how much time-frequency components contribute to the correct discrimination among experimental conditions. The methodology introduced in the present study may be extended to other analyses of neural data, enabling the discovery of data features that are modulated by the experimental manipulation.
    Language English
    Publishing date 2022-04-14
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2452979-5
    ISSN 1662-5196
    ISSN 1662-5196
    DOI 10.3389/fninf.2022.871904
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: A study of autoencoders as a feature extraction technique for spike sorting.

    Ardelean, Eugen-Richard / Coporîie, Andreea / Ichim, Ana-Maria / Dînșoreanu, Mihaela / Mureșan, Raul Cristian

    PloS one

    2023  Volume 18, Issue 3, Page(s) e0282810

    Abstract: Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current ...

    Abstract Spike sorting is the process of grouping spikes of distinct neurons into their respective clusters. Most frequently, this grouping is performed by relying on the similarity of features extracted from spike shapes. In spite of recent developments, current methods have yet to achieve satisfactory performance and many investigators favour sorting manually, even though it is an intensive undertaking that requires prolonged allotments of time. To automate the process, a diverse array of machine learning techniques has been applied. The performance of these techniques depends however critically on the feature extraction step. Here, we propose deep learning using autoencoders as a feature extraction method and evaluate extensively the performance of multiple designs. The models presented are evaluated on publicly available synthetic and real "in vivo" datasets, with various numbers of clusters. The proposed methods indicate a higher performance for the process of spike sorting when compared to other state-of-the-art techniques.
    MeSH term(s) Algorithms ; Action Potentials/physiology ; Machine Learning ; Neurons/physiology ; Signal Processing, Computer-Assisted
    Language English
    Publishing date 2023-03-09
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0282810
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Sharp detection of oscillation packets in rich time-frequency representations of neural signals.

    Ardelean, Eugen-Richard / Bârzan, Harald / Ichim, Ana-Maria / Mureşan, Raul Cristian / Moca, Vasile Vlad

    Frontiers in human neuroscience

    2023  Volume 17, Page(s) 1112415

    Abstract: Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication ... ...

    Abstract Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets' precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations' dynamics.
    Language English
    Publishing date 2023-12-07
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2425477-0
    ISSN 1662-5161
    ISSN 1662-5161
    DOI 10.3389/fnhum.2023.1112415
    Database MEDical Literature Analysis and Retrieval System OnLINE

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