Relevant bibliographies by topics / SPIKE-distance

Academic literature on the topic 'SPIKE-distance'

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Published: 10 March 2023
Last updated: 11 March 2023

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Journal articles on the topic "SPIKE-distance"

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Kreuz, Thomas. "SPIKE-distance." Scholarpedia 7, no. 12 (2012): 30652. http://dx.doi.org/10.4249/scholarpedia.30652.

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Rossum, M. C. W. van. "A Novel Spike Distance." Neural Computation 13, no. 4 (April 1, 2001): 751–63. http://dx.doi.org/10.1162/089976601300014321.

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The discrimination between two spike trains is a fundamental problem for both experimentalists and the nervous system itself. We introduce a measure for the distance between two spike trains. The distance has a time constant as a parameter. Depending on this parameter, the distance interpolates between a coincidence detector and a rate difference counter. The dependence of the distance on noise is studied with an integrate-andfire model. For an intermediate range of the time constants, the distance depends linearly on the noise. This property can be used to determine the intrinsic noise of a neuron.
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Fu, Q. G., D. Flament, J. D. Coltz, and T. J. Ebner. "Relationship of Cerebellar Purkinje Cell Simple Spike Discharge to Movement Kinematics in the Monkey." Journal of Neurophysiology 78, no. 1 (July 1, 1997): 478–91. http://dx.doi.org/10.1152/jn.1997.78.1.478.

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Fu, Q.-G., D. Flament, J. D. Coltz, and T. J. Ebner. Relationship of cerebellar Purkinje cell simple spike discharge to movement kinematics in the monkey. J. Neurophysiol. 78: 478–491, 1997. The simple spike discharge of 231 cerebellar Purkinje cells in ipsilateral lobules V and VI was recorded in three monkeys trained to perform a visually guided reaching task requiring movements of different directions and distances. The discharge of 179 cells was significantly modulated during movement to one or more targets. Mean simple spike rate was fitted to a cosine function for direction tuning, a simple linear function for distance modulation, and a multiple linear regression model that included terms for direction, distance, and target position. On the basis of the fit to the direction and distance models, there were more distance-related than direction-related Purkinje cells. The simple spike discharge of most direction-related cells modulated at only one target distance. The preferred directions for the simple spike tuning were not uniformly distributed across the workspace. The discharge of most distance-related cells modulated along only one movement direction. On the basis of the multiple linear regression model, simple spike discharge was also correlated with target position, in addition to direction and distance. Approximately half of the Purkinje cells had simple spike activity associated with only a single parameter, and only a small fraction of the cells with all three. The multiple regression model was extended to evaluate the correlations as a function of time. Considerable overlap occurred in the timing of the simple spike correlations with the parameters. The latency for correlation with movement direction occurred mainly in a 500-ms interval centered on movement onset. The correlations with target position also occurred around movement onset, in the range of −200–500 ms. Distance correlations were more variable, with onset latencies from −500 to 1,000 ms. These results demonstrate that the simple spike discharge of cerebellar Purkinje cells is correlated with movement direction, distance, and target position. Comparing these results to motor cortical discharge shows that the correlations with these parameters were weaker in Purkinje cell simple spike discharge, and that, for the majority of Purkinje cells, the simple spike discharge was significantly related to only a single movement parameter. Other differences between simple spike responses and those of motor cortical cells include the nonuniform distribution of preferred directions and the extensive overlap in the timing of the correlations. These differences suggest that Purkinje cells process, encode, and use kinematic information differently than motor cortical neurons.
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Kreuz, Thomas, Daniel Chicharro, Conor Houghton, Ralph G. Andrzejak, and Florian Mormann. "Monitoring spike train synchrony." Journal of Neurophysiology 109, no. 5 (March 1, 2013): 1457–72. http://dx.doi.org/10.1152/jn.00873.2012.

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Recently, the SPIKE-distance has been proposed as a parameter-free and timescale-independent measure of spike train synchrony. This measure is time resolved since it relies on instantaneous estimates of spike train dissimilarity. However, its original definition led to spuriously high instantaneous values for eventlike firing patterns. Here we present a substantial improvement of this measure that eliminates this shortcoming. The reliability gained allows us to track changes in instantaneous clustering, i.e., time-localized patterns of (dis)similarity among multiple spike trains. Additional new features include selective and triggered temporal averaging as well as the instantaneous comparison of spike train groups. In a second step, a causal SPIKE-distance is defined such that the instantaneous values of dissimilarity rely on past information only so that time-resolved spike train synchrony can be estimated in real time. We demonstrate that these methods are capable of extracting valuable information from field data by monitoring the synchrony between neuronal spike trains during an epileptic seizure. Finally, the applicability of both the regular and the real-time SPIKE-distance to continuous data is illustrated on model electroencephalographic (EEG) recordings.
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Rusu, Cătălin V., and Răzvan V. Florian. "A New Class of Metrics for Spike Trains." Neural Computation 26, no. 2 (February 2014): 306–48. http://dx.doi.org/10.1162/neco_a_00545.

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The distance between a pair of spike trains, quantifying the differences between them, can be measured using various metrics. Here we introduce a new class of spike train metrics, inspired by the Pompeiu-Hausdorff distance, and compare them with existing metrics. Some of our new metrics (the modulus-metric and the max-metric) have characteristics that are qualitatively different from those of classical metrics like the van Rossum distance or the Victor and Purpura distance. The modulus-metric and the max-metric are particularly suitable for measuring distances between spike trains where information is encoded in bursts, but the number and the timing of spikes inside a burst do not carry information. The modulus-metric does not depend on any parameters and can be computed using a fast algorithm whose time depends linearly on the number of spikes in the two spike trains. We also introduce localized versions of the new metrics, which could have the biologically relevant interpretation of measuring the differences between spike trains as they are perceived at a particular moment in time by a neuron receiving these spike trains.
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Lou, Yanhong, Liang Chen, Qingguo Xu, and Xunzhong Zhang. "Genotypic Variation of Morphological Traits in Tall Fescue (Festuca arundinacea Schreb.) Accessions." HortScience 50, no. 4 (April 2015): 512–16. http://dx.doi.org/10.21273/hortsci.50.4.512.

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Tall fescue is an important cool-season grass widely used for forage and turf, and its genotypic variation for morphological traits has not been well documented. One hundred and fifteen tall fescue accessions, including 25 commercial cultivars, were divided into five groups based on their origination. The morphological traits, including plant height, spike length, pulvinus distance, spikelet count, branch count per spike, spike count per plant, and spike weight in different accessions were determined under field conditions in 2013 and 2014. There was significant genotypic variation in morphological traits among the 115 tall fescue accessions. Wild accessions exhibited a greater variation in the morphological traits than commercial cultivars. Close correlations were found among plant height, spike length, pulvinus distance, and spikelet count. The results of this suggest plant height, spike length, pulvinus distance, and spikelet count could be used as key morphological traits for evaluating all fescue germplasm effectively.
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Kreuz, Thomas, Mario Mulansky, and Nebojsa Bozanic. "SPIKY: a graphical user interface for monitoring spike train synchrony." Journal of Neurophysiology 113, no. 9 (May 2015): 3432–45. http://dx.doi.org/10.1152/jn.00848.2014.

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Techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of experimental spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years three time-resolved measures of spike train synchrony have been proposed, the ISI-distance, the SPIKE-distance, and event synchronization. The Matlab source codes for calculating and visualizing these measures have been made publicly available. However, due to the many different possible representations of the results the use of these codes is rather complicated and their application requires some basic knowledge of Matlab. Thus it became desirable to provide a more user-friendly and interactive interface. Here we address this need and present SPIKY, a graphical user interface that facilitates the application of time-resolved measures of spike train synchrony to both simulated and real data. SPIKY includes implementations of the ISI-distance, the SPIKE-distance, and the SPIKE-synchronization (an improved and simplified extension of event synchronization) that have been optimized with respect to computation speed and memory demand. It also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels.
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Christen, Markus, Adam Kohn, Thomas Ott, and Ruedi Stoop. "Measuring spike pattern reliability with the Lempel–Ziv-distance." Journal of Neuroscience Methods 156, no. 1-2 (September 2006): 342–50. http://dx.doi.org/10.1016/j.jneumeth.2006.02.023.

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Zhu, Jinjie, and Xianbin Liu. "Measuring spike timing distance in the Hindmarsh–Rose neurons." Cognitive Neurodynamics 12, no. 2 (December 27, 2017): 225–34. http://dx.doi.org/10.1007/s11571-017-9466-9.

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Kang, Kukjin, and Shun-ichi Amari. "Discrimination with Spike Times and ISI Distributions." Neural Computation 20, no. 6 (June 2008): 1411–26. http://dx.doi.org/10.1162/neco.2007.07-07-561.

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We study the discrimination capability of spike time sequences using the Chernoff distance as a metric. We assume that spike sequences are generated by renewal processes and study how the Chernoff distance depends on the shape of interspike interval (ISI) distribution. First, we consider a lower bound to the Chernoff distance because it has a simple closed form. Then we consider specific models of ISI distributions such as the gamma, inverse gaussian (IG), exponential with refractory period (ER), and that of the leaky integrate-and-fire (LIF) neuron. We found that the discrimination capability of spike times strongly depends on high-order moments of ISI and that it is higher when the spike time sequence has a larger skewness and a smaller kurtosis. High variability in terms of coefficient of variation (CV) does not necessarily mean that the spike times have less discrimination capability. Spike sequences generated by the gamma distribution have the minimum discrimination capability for a given mean and variance of ISI. We used series expansions to calculate the mean and variance of ISIs for LIF neurons as a function of the mean input level and the input noise variance. Spike sequences from an LIF neuron are more capable of discrimination than those of IG and gamma distributions when the stationary voltage level is close to the neuron's threshold value of the neuron.
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Dissertations / Theses on the topic "SPIKE-distance"

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BOZANIC, NEBOJSA. "Measures of spike train synchrony." Doctoral thesis, 2016. http://hdl.handle.net/2158/1043650.

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In experimental neuroscience techniques for recording large-scale neuronal spiking activity are developing very fast. This leads to an increasing demand for algorithms capable of analyzing large amounts of spike train data. One of the most crucial and demanding tasks is the identification of similarity patterns with a very high temporal resolution and across different spatial scales. To address this task, in recent years time-resolved measures of spike train synchrony such as the ISI-distance and the SPIKE-distance have been proposed. Here we add the complementary measure SPIKE-synchronization, a sophisticated multivariate coincidence detector with a very intuitive interpretation. In the first Results chapter we present SPIKY, an interactive graphical user interface that facilitates the application of these three time-resolved measures of spike train synchrony to both simulated and real data. SPIKY, which has been optimized with respect to computation speed and memory demand, also comprises a spike train generator and an event detector that makes it capable of analyzing continuous data. Finally, the SPIKY package includes additional complementary programs aimed at the analysis of large numbers of datasets and the estimation of significance levels. In the second Results chapter we deal with the very important problem of latency variations in real data. By means of a validated setup we can show that the parameter-free SPIKE-distance outperforms two time-scale dependent standard measures. In summary, in this thesis we provide several important measures and corrections that when applied to the right experimental datasets could potentially lead to an increased understanding of the neural code - the ultimate goal of neuroscience.
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SATUVUORI, EERO ANTERO. "Spike train distances and neuronal coding." Doctoral thesis, 2019. http://hdl.handle.net/2158/1153169.

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The thesis is from the field of data-analysis and computational neuroscience. In the thesis I improved existing spike train distance measures and developed new ones. The main methods used are ISI-distance, SPIKE-distance and SPIKE-Synchronization.
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Book chapters on the topic "SPIKE-distance"

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Victor, Jonathan D. "Spike Train Distance." In Encyclopedia of Computational Neuroscience, 2808–14. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_409.

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Victor, Jonathan D. "Spike Train Distance." In Encyclopedia of Computational Neuroscience, 1–8. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_409-1.

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Kreuz, Thomas, Conor Houghton, and Jonathan D. Victor. "Spike Train Distance." In Encyclopedia of Computational Neuroscience, 1–14. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4614-7320-6_409-2.

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Kuroda, Kaori, and Mikio Hasegawa. "Method for Estimating Neural Network Topology Based on SPIKE-Distance." In Artificial Neural Networks and Machine Learning – ICANN 2016, 91–98. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44778-0_11.

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Chiang, Wen-Hao, Baichuan Yuan, Hao Li, Bao Wang, Andrea Bertozzi, Jeremy Carter, Brad Ray, and George Mohler. "SOS-EW: System for Overdose Spike Early Warning Using Drug Mover’s Distance-Based Hawkes Processes." In Machine Learning and Knowledge Discovery in Databases, 538–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43823-4_43.

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Samura, Toshikazu, Yutaka Sakai, Hatsuo Hayashi, and Takeshi Aihara. "Distance- and Direction-Dependent Synaptic Weight Distributions for Directional Spike Propagation in a Recurrent Network: Self-actuated Shutdown of Synaptic Plasticity." In Neural Information Processing, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-42051-1_1.

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Conference papers on the topic "SPIKE-distance"

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Shekhar, Shubhanshu, and Kaushik Majumdar. "A new spike train distance measure." In 2012 International Conference on Data Science & Engineering (ICDSE). IEEE, 2012. http://dx.doi.org/10.1109/icdse.2012.6281906.

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Guo, Shengpeng, Liyuan Guo, Seyed Mohammad Ali Zeinolabedin, and Christian Mayr. "Various Distance Metrics Evaluation on Neural Spike Classification." In 2022 IEEE Biomedical Circuits and Systems Conference (BioCAS). IEEE, 2022. http://dx.doi.org/10.1109/biocas54905.2022.9948670.

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Ande, Sathish, Jayanth R. Regatti, Neha Pandey, Ajith Karunarathne, Lopamudra Giri, and Soumya Jana. "Heterogeneity in Neuronal Calcium Spike Trains based on Empirical Distance." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441175.

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Iskarous, Mark M., Sriramana Sankar, Qianwei Li, Christopher L. Hunt, and Nitish V. Thakor. "A scalable algorithm based on spike train distance to select stimulation patterns for sensory feedback." In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441155.

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Mast, Timothy, Yu Pan, Carvel Holton, and Mehdi Ahmadian. "Intermediate Distance Testing of Optical Top-of-Rail (TOR) Lubricity Sensors on a Remote-Controlled Rail Cart." In 2021 Joint Rail Conference. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/jrc2021-58347.

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Abstract The primary intent of this study is to evaluate the effectiveness and utility of a laser-based measurement unit for qualitative assessment of the presence and amount of Top-of-Rail Friction Modifier (TOR or TORFM), at reasonably high speeds and over long distances in the field. As a capstone to this phase of development, a series of field tests were conducted on revenue service track in partnership with a local Class 1 railroad. For these tests, the Third Generation Rail Lubricity Sensor was mounted on a Remote-Controlled Rail Cart and tested continuously over several miles of track. This longer window is able to cover the domain of multiple wayside applicators over a distance of more than 3 miles, the expected carry distance of TORFM. The results of this testing demonstrate the capacity of optical sensors to measure and evaluate track lubricity. The signal characteristics at or near wayside applicators demonstrate a clear impulse from the heavy lubricant close to the applicator. Further, by collecting continuous data down track from a wayside applicator it is possible to observe several novel ways in which the TORFM and flange grease carries beyond the point of application. One such example is a clear spike in track lubricity when entering or exiting curves caused by the lateral shift of the wheelsets drawing fresh lubricant previously out of contact with the rail into contact creating a “phantom applicator” effect. These observations are crucial to understanding in detail the way the TORFM and flange grease is carried down track. They are also essential to creating predictive models for most effective application of friction modifiers to specific track geometries.
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Walraevens, R. E., and N. A. Cumpsty. "Leading Edge Separation Bubbles on Turbomachine Blades." In ASME 1993 International Gas Turbine and Aeroengine Congress and Exposition. American Society of Mechanical Engineers, 1993. http://dx.doi.org/10.1115/93-gt-091.

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Results are presented for separation bubbles of the type which can form near the leading edges of thin compressor or turbine blades. These often occur when the incidence is such that the stagnation point is not on the nose of the aerofoil. Tests were carried out at low speed on a single aerofoil to simulate the range of conditions found on compressor blades. Both circular and elliptic shapes of leading edge were tested. Results are presented for a range of incidence, Reynolds number and turbulence intensity and scale. The principal quantitative measurements presented are the pressure distributions in the leading edge and bubble region, as well as the boundary layer properties at a fixed distance downstream where most of the flows had reattached. Reynolds number was found to have a comparatively small influence, but a raised level of freestream turbulence has a striking effect, shortening or eliminating the bubble and increasing the magnitude of the suction spike. Increased freestream turbulence also reduces the boundary layer thickness and shape parameter after the bubble. Some explanations of the processes are outlined.
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Kawaguchi, Chihiro, Masateru Taniguchi, Makusu Tsutsui, Satoyuki Kawano, and Tomoji Kawai. "Electrical Detection of Pollen Allergen Using Electrode-Embedded-Micro-Channel." In ASME-JSME-KSME 2011 Joint Fluids Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/ajk2011-36035.

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Hay fever is a disease that approximately 20 percent of the world population develop. Providing appropriate treatment for this plant-specific pollen allergy requires a method to identify various types of pollen. Here we introduce a technique that can be used for direct detections of single biological macromolecules such as pollen. Our method is based on two-probe current measurements using a microfluidic-channel-embedded-electrode system. A fabrication procedure of the device is as follows. First, we formed two gold nanoelectrodes by standard electron beam lithography and lift-off processes. The interelectrode gap distance was designed to be 500 nm. We then deposited a SiO2 layer. After that, we fabricated Cr etching mask. Finally, we dry-etched the sample by reactive ion etching and obtained a microfluidic-channel-embedded-electrode structure. We flowed HEPES(2-[4-(2-Hydroxyethyl)-1-piperazinyl] ethanesulfonic acid) solution containing pollen of 500 nm size into the micro-channel and simultaneously measured current flowing through the two electrodes. Characteristic spike-like signals were observed; sharp rise of the current followed by a rather smooth decrease to the base current level. We exhibited control experiments in a HEPES solution wherein no pollen was added and observed only featureless current traces. We anticipate that the sharp current rise is associated with trapping of a single cedar pollen between the electrodes whereas the gradual current decrease represents the pollen detrapping. We propose the technique for identifying several kinds of pollens based on the height and the width of the current spikes.
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