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Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength
by
Kessler, Dominik
, Ashida, Go
, Carr, Catherine E.
, Kretzberg, Jutta
in
auditory brainstem
/ Auditory nerve
/ Auditory pathways
/ Auditory system
/ autocorrelogram
/ Brain stem
/ circular statistics
/ Firing pattern
/ Frequency dependence
/ Hearing
/ Histograms
/ Information processing
/ Nervous system
/ Neurons
/ Neuroscience
/ Periodicity
/ phase-locking
/ spike train analysis
/ temporal coding
2021
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Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength
by
Kessler, Dominik
, Ashida, Go
, Carr, Catherine E.
, Kretzberg, Jutta
in
auditory brainstem
/ Auditory nerve
/ Auditory pathways
/ Auditory system
/ autocorrelogram
/ Brain stem
/ circular statistics
/ Firing pattern
/ Frequency dependence
/ Hearing
/ Histograms
/ Information processing
/ Nervous system
/ Neurons
/ Neuroscience
/ Periodicity
/ phase-locking
/ spike train analysis
/ temporal coding
2021
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength
by
Kessler, Dominik
, Ashida, Go
, Carr, Catherine E.
, Kretzberg, Jutta
in
auditory brainstem
/ Auditory nerve
/ Auditory pathways
/ Auditory system
/ autocorrelogram
/ Brain stem
/ circular statistics
/ Firing pattern
/ Frequency dependence
/ Hearing
/ Histograms
/ Information processing
/ Nervous system
/ Neurons
/ Neuroscience
/ Periodicity
/ phase-locking
/ spike train analysis
/ temporal coding
2021
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Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength
Journal Article
Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength
2021
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Overview
Information processing in the nervous system critically relies on temporally precise spiking activity. In the auditory system, various degrees of phase-locking can be observed from the auditory nerve to cortical neurons. The classical metric for quantifying phase-locking is the vector strength (VS), which captures the periodicity in neuronal spiking. More recently, another metric, called the correlation index (CI), was proposed to quantify the temporally reproducible response characteristics of a neuron. The CI is defined as the peak value of a normalized shuffled autocorrelogram (SAC). Both VS and CI have been used to investigate how temporal information is processed and propagated along the auditory pathways. While previous analyses of physiological data in cats suggested covariation of these two metrics, general characterization of their connection has never been performed. In the present study, we derive a rigorous relationship between VS and CI. To model phase-locking, we assume Poissonian spike trains with a temporally changing intensity function following a von Mises distribution. We demonstrate that VS and CI are mutually related via the so-called concentration parameter that determines the degree of phase-locking. We confirm that these theoretical results are largely consistent with physiological data recorded in the auditory brainstem of various animals. In addition, we generate artificial phase-locked spike sequences, for which recording and analysis parameters can be systematically manipulated. Our analysis results suggest that mismatches between empirical data and the theoretical prediction can often be explained with deviations from the von Mises distribution, including skewed or multimodal period histograms. Furthermore, temporal relations of spike trains across trials can contribute to higher CI values than predicted mathematically based on the VS. We find that, for most applications, a SAC bin width of 50 ms seems to be a favorable choice, leading to an estimated error below 2.5% for physiologically plausible conditions. Overall, our results provide general relations between the two measures of phase-locking and will aid future analyses of different physiological datasets that are characterized with these metrics.
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