Literature DB >> 23982848

Vector strength after Goldberg, Brown, and von Mises: biological and mathematical perspectives.

J Leo van Hemmen1.   

Abstract

The vector strength, a number between 0 and 1, is a classical notion in biology. It was first used in neurobiology by Goldberg and Brown (J Neurophys 31:639-656, 1969) but dates back at least to von Mises (Phys Z 19:490-500, 1918). It is widely used as a means to measure the periodicity or lack of periodicity of a neuronal response to an outside periodic signal. Here, we provide a self-contained and simple treatment of a closely related notion, the synchrony vector, a complex number with the vector strength as its absolute value and with a definite phase that one can directly relate to a biophysical delay. The present analysis is essentially geometrical and based on convexity. As such it does two things. First, it maps a sequence of points, events such as spike times on the time axis, onto the unit circle in the complex plane so that for a perfectly periodic repetition, a single point on the unit circle appears. Second, events hardly ever occur periodically, so that we need a criterion of how to extract periodicity out of a set of real numbers. It is here where convex geometry comes in, and a geometrically intuitive picture results. We also quantify how the events cluster around a period as the vector strength goes to 1. A typical example from the auditory system is used to illustrate the general considerations. Furthermore, von Mises' seminal contribution to the notion of vector strength is explained in detail. Finally, we generalize the synchrony vector to a function of angular frequency, not fixed on the input frequency at hand and indicate its potential as a "resonating" vector strength.

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Year:  2013        PMID: 23982848     DOI: 10.1007/s00422-013-0561-7

Source DB:  PubMed          Journal:  Biol Cybern        ISSN: 0340-1200            Impact factor:   2.086


  6 in total

1.  Phase Locking of Auditory-Nerve Fibers Reveals Stereotyped Distortions and an Exponential Transfer Function with a Level-Dependent Slope.

Authors:  Adam J Peterson; Peter Heil
Journal:  J Neurosci       Date:  2019-03-13       Impact factor: 6.167

2.  Emphasis of spatial cues in the temporal fine structure during the rising segments of amplitude-modulated sounds II: single-neuron recordings.

Authors:  Mathias Dietz; Torsten Marquardt; Annette Stange; Michael Pecka; Benedikt Grothe; David McAlpine
Journal:  J Neurophysiol       Date:  2014-02-19       Impact factor: 2.714

3.  Computational Modeling of Synchrony in the Auditory Nerve in Response to Acoustic and Electric Stimulation.

Authors:  Raymond L Goldsworthy
Journal:  Front Comput Neurosci       Date:  2022-06-17       Impact factor: 3.387

4.  Ergodicity and parameter estimates in auditory neural circuits.

Authors:  Peter G Toth; Petr Marsalek; Ondrej Pokora
Journal:  Biol Cybern       Date:  2017-10-29       Impact factor: 2.086

5.  Theoretical Relationship Between Two Measures of Spike Synchrony: Correlation Index and Vector Strength.

Authors:  Dominik Kessler; Catherine E Carr; Jutta Kretzberg; Go Ashida
Journal:  Front Neurosci       Date:  2021-12-20       Impact factor: 4.677

6.  Mathematization of nature: how it is done.

Authors:  J Leo van Hemmen
Journal:  Biol Cybern       Date:  2021-12       Impact factor: 2.086

  6 in total

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