NASS: An empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology

Adamos, Dimitrios A.; Laskaris, Nikolaos A.; Kosmidis, Efstratios K.; Theophilidis, George

doi:10.1016/j.jneumeth.2010.04.018

Cited by 26 publications

(18 citation statements)

References 32 publications

(48 reference statements)

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“…The second output F( m ) is the particular value of objective function that measures the cohesiveness of the detected dominant-set. The overall clustering procedure, proceeds in iterative fashion (the matlab code can be found from [42]). The points participating in the dominant-set (at the end of a single execution of the Dominant_Set-routine) are removed from the set and the associated entries in matrix A are eliminated.…”

Section: Methodsmentioning

confidence: 99%

Functional Aspects of the EGF-Induced MAP Kinase Cascade: A Complex Self-Organizing System Approach

et al. 2014

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The EGF-induced MAP kinase cascade is one of the most important and best characterized networks in intracellular signalling. It has a vital role in the development and maturation of living organisms. However, when deregulated, it is involved in the onset of a number of diseases. Based on a computational model describing a “surface” and an “internalized” parallel route, we use systems biology techniques to characterize aspects of the network’s functional organization. We examine the re-organization of protein groups from low to high external stimulation, define functional groups of proteins within the network, determine the parameter best encoding for input intensity and predict the effect of protein removal to the system’s output response. Extensive functional re-organization of proteins is observed in the lower end of stimulus concentrations. As we move to higher concentrations the variability is less pronounced. 6 functional groups have emerged from a consensus clustering approach, reflecting different dynamical aspects of the network. Mutual information investigation revealed that the maximum activation rate of the two output proteins best encodes for stimulus intensity. Removal of each protein of the network resulted in a range of graded effects, from complete silencing to intense activation. Our results provide a new “vista” of the EGF-induced MAP kinase cascade, from the perspective of complex self-organizing systems. Functional grouping of the proteins reveals an organizational scheme contrasting the current understanding of modular topology. The six identified groups may provide the means to experimentally follow the dynamics of this complex network. Also, the vulnerability analysis approach may be used for the development of novel therapeutic targets in the context of personalized medicine.

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Section: Methodsmentioning

confidence: 99%

Functional Aspects of the EGF-Induced MAP Kinase Cascade: A Complex Self-Organizing System Approach

et al. 2014

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“…More or less all the existing machine algorithms have been executed in this field. The improved classified efficiency is proved in [5][6][7][8][9].It also proves its betterment in the field of image processing by generating high resolution images from low resolution input [3,[10][11].ELM also entered in the field of system modeling and prediction [12][13].…”

Section: Applications Of Elmmentioning

confidence: 96%

A Survey on Study of Various Machine Learning Methods for Classification

Subramanyam¹,

Pugazendi²

2015

IJDTA

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“…Perhaps the most important feature of spike sorting methods that we have not explicitly included in this model is "overlapping spikes" [1], [5], [13], [18], [30], [33], [37]. Preliminary analysis of our model in this regime (not shown), inspired by reviewer comments, demonstrated to us that while the FMM-DL as written is insufficient to address this issue, a minor modification to FMM-DL will enable "demixing" overlapping spikes.…”

Section: B Future Directionsmentioning

confidence: 99%

Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling

Carlson

Vogelstein

et al. 2014

IEEE Trans. Biomed. Eng.

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We propose a methodology for joint feature learning and clustering of multichannel extracellular electrophysiological data, across multiple recording periods for action potential detection and classification (sorting). Our methodology improves over the previous state of the art principally in four ways. First, via sharing information across channels, we can better distinguish between single-unit spikes and artifacts. Second, our proposed "focused mixture model" (FMM) deals with units appearing, disappearing, or reappearing over multiple recording days, an important consideration for any chronic experiment. Third, by jointly learning features and clusters, we improve performance over previous attempts that proceeded via a two-stage learning process. Fourth, by directly modeling spike rate, we improve the detection of sparsely firing neurons. Moreover, our Bayesian methodology seamlessly handles missing data. We present the state-of-the-art performance without requiring manually tuning hyperparameters, considering both a public dataset with partial ground truth and a new experimental dataset.

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NASS: An empirical approach to spike sorting with overlap resolution based on a hybrid noise-assisted methodology

Cited by 26 publications

References 32 publications

Functional Aspects of the EGF-Induced MAP Kinase Cascade: A Complex Self-Organizing System Approach

Functional Aspects of the EGF-Induced MAP Kinase Cascade: A Complex Self-Organizing System Approach

A Survey on Study of Various Machine Learning Methods for Classification

Multichannel Electrophysiological Spike Sorting via Joint Dictionary Learning and Mixture Modeling

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