A novel method for quantifying periodicity and time delay in dynamic neural networks using unstable subaction potential threshold depolarizations.
J Neurophysiol
; 123(3): 1236-1246, 2020 03 01.
Article
em En
| MEDLINE
| ID: mdl-31995437
ABSTRACT
Techniques to identify and correlate the propagation of electrical signals (like action potentials) along neural networks are well described, using multisite recordings. In these cases, the waveform of action potentials is usually relatively stable and discriminating relevant electrical signals straightforward. However, problems can arise when attempting to identify and correlate the propagation of signals when their waveforms are unstable (e.g., fluctuations in amplitude or time course). This makes correlation of the degree of synchronization and time lag between propagating electrical events across two or more recording sites problematic. Here, we present novel techniques for the determination of the periodicity of electrical signals at individual sites. When recording from two independent sites, we present novel analytical techniques for joint determination of periodicity and time delay. The techniques presented exploit properties of the cross-correlation function, rather than utilizing the time lag at which the cross-correlation function is maximized. The approach allows determination of directionality of the spread of excitation along a neural network based on measurements of the time delay between recording sites. This new method is particularly applicable to analysis of signals in other biological systems that have unstable characteristics in waveform that show dynamic variability.NEW & NOTEWORTHY The determination of frequency(s) at which two sources are synchronized, and relative time delay between them, is a fundamental problem for a wide a range of signal-processing applications. In this methodology paper, we present novel procedures for periodicity estimation for single time series and joint periodicity and time delay estimation for two time series. The methods use properties of the cross-correlation function rather than the cross-correlation function explicitly.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Periodicidade
/
Processamento de Sinais Assistido por Computador
/
Potenciais de Ação
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Sistema Nervoso Entérico
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Modelos Teóricos
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Rede Nervosa
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Revista:
J Neurophysiol
Ano de publicação:
2020
Tipo de documento:
Article
País de afiliação:
Austrália