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      • Record: found
      • Abstract: found
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      Stability of Neural Firing in the Trigeminal Nuclei under Mechanical Whisker Stimulation

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          Abstract

          Sensory information handling is an essentially nonstationary process even under a periodic stimulation. We show how the time evolution of ridges in the wavelet spectrum of spike trains can be used for quantification of the dynamical stability of the neuronal responses to a stimulus. We employ this method to study neuronal responses in trigeminal nuclei of the rat provoked by tactile whisker stimulation. Neurons from principalis (Pr5) and interpolaris (Sp5i) show the maximal stability at the intermediate (50 ms) stimulus duration, whereas Sp5o cells “prefer” shorter (10 ms) stimulation. We also show that neurons in all three nuclei can perform as stimulus frequency filters. The response stability of about 33% of cells exhibits low-pass frequency dynamics. About 57% of cells have band-pass dynamics with the optimal frequency at 5 Hz for Pr5 and Sp5i, and 4 Hz for Sp5o, and the remaining 10% show no prominent dependence on the stimulus frequency. This suggests that the neural coding scheme in trigeminal nuclei is not fixed, but instead it adapts to the stimulus characteristics.

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          Unsupervised spike detection and sorting with wavelets and superparamagnetic clustering.

          R. Quian Quiroga, Z Nádasdy, Y. BEN-SHAUL (2004)
          This study introduces a new method for detecting and sorting spikes from multiunit recordings. The method combines the wavelet transform, which localizes distinctive spike features, with superparamagnetic clustering, which allows automatic classification of the data without assumptions such as low variance or gaussian distributions. Moreover, an improved method for setting amplitude thresholds for spike detection is proposed. We describe several criteria for implementation that render the algorithm unsupervised and fast. The algorithm is compared to other conventional methods using several simulated data sets whose characteristics closely resemble those of in vivo recordings. For these data sets, we found that the proposed algorithm outperformed conventional methods.
          Article 0 comments Cited 534 times – based on 0 reviews     Review now
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            Neuronal spike trains and stochastic point processes. II. Simultaneous spike trains.

            D H Perkel, G L Gerstein, G P Moore (1967)
            The statistical analysis of two simultaneously observed trains of neuronal spikes is described, using as a conceptual framework the theory of stochastic point processes.The first statistical question that arises is whether the observed trains are independent; statistical techniques for testing independence are developed around the notion that, under the null hypothesis, the times of spike occurrence in one train represent random instants in time with respect to the other. If the null hypothesis is rejected-if dependence is attributed to the trains-the problem then becomes that of characterizing the nature and source of the observed dependencies. Statistical signs of various classes of dependencies, including direct interaction and shared input, are discussed and illustrated through computer simulations of interacting neurons. The effects of nonstationarities on the statistical measures for simultaneous spike trains are also discussed. For two-train comparisons of irregularly discharging nerve cells, moderate nonstationarities are shown to have little effect on the detection of interactions.Combining repetitive stimulation and simultaneous recording of spike trains from two (or more) neurons yields additional clues as to possible modes of interaction among the monitored neurons; the theory presented is illustrated by an application to experimentally obtained data from auditory neurons.A companion paper covers the analysis of single spike trains.
            Article 0 comments Cited 143 times – based on 0 reviews     Review now
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              Sampling properties of the spectrum and coherency of sequences of action potentials.

              Partha Mitra, M. Jarvis (2001)
              The spectrum and coherency are useful quantities for characterizing the temporal correlations and functional relations within and between point processes. This article begins with a review of these quantities, their interpretation, and how they may be estimated. A discussion of how to assess the statistical significance of features in these measures is included. In addition, new work is presented that builds on the framework established in the review section. This work investigates how the estimates and their error bars are modified by finite sample sizes. Finite sample corrections are derived based on a doubly stochastic inhomogeneous Poisson process model in which the rate functions are drawn from a low-variance gaussian process. It is found that in contrast to continuous processes, the variance of the estimators cannot be reduced by smoothing beyond a scale set by the number of point events in the interval. Alternatively, the degrees of freedom of the estimators can be thought of as bounded from above by the expected number of point events in the interval. Further new work describing and illustrating a method for detecting the presence of a line in a point process spectrum is also presented, corresponding to the detection of a periodic modulation of the underlying rate. This work demonstrates that a known statistical test, applicable to continuous processes, applies with little modification to point process spectra and is of utility in studying a point process driven by a continuous stimulus. Although the material discussed is of general applicability to point processes, attention will be confined to sequences of neuronal action potentials (spike trains), the motivation for this work.
              Article 0 comments Cited 100 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Comput Intell Neurosci
                Journal ID (publisher-id): CIN
                Title: Computational Intelligence and Neuroscience
                Publisher: Hindawi Publishing Corporation
                ISSN (Print): 1687-5265
                ISSN (Electronic): 1687-5273
                Publication date (Print): 2010
                Publication date (Electronic): 6 January 2010
                Publication date PMC-release: 6 January 2010
                Volume: 2010
                Electronic Location Identifier: 340541
                Affiliations
                1Departamento de Matemática Aplicada, Universidad Complutense de Madrid, Avenida Complutense s/n, 28040 Madrid, Spain
                2Radiophysics and Nonlinear Dynamics Chair, Physics Department, Saratov State University, Astrakhanskaya Str. 83, Saratov 410026, Russia
                3Neurocomputing and Neurorobotics Group, Universidad Complutense de Madrid, Avenida Arcos de Jalón s/n, 28037 Madrid, Spain
                Author notes
                *Valeri A. Makarov: vmakarov@ 123456mat.ucm.es

                Academic Editor: Rodrigo Quian Quiroga

                Article
                DOI: 10.1155/2010/340541
                PMC ID: 2810455
                PubMed ID: 20111733
                SO-VID: 79993dc3-27b5-40cc-8f2e-e143b114676f
                Copyright © Copyright © 2010 Valeri A. Makarov et al.
                License:

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 27 February 2009
                Date revision received : 22 June 2009
                Date accepted : 12 October 2009
                Categories
                Subject: Research Article

                ScienceOpen disciplines: Neurosciences
                Data availability:
                ScienceOpen disciplines: Neurosciences

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