Fluctuation scaling in neural spike trains

Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power function relationships between the mean and variance of either inter-event intervals or counting statistics, depending on meas...

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Main Authors: Shinsuke Koyama, Ryota Kobayashi
Format: Article
Language:English
Published: AIMS Press 2015-12-01
Series:Mathematical Biosciences and Engineering
Subjects:
Online Access:https://www.aimspress.com/article/doi/10.3934/mbe.2016006
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author Shinsuke Koyama
Ryota Kobayashi
author_facet Shinsuke Koyama
Ryota Kobayashi
author_sort Shinsuke Koyama
collection DOAJ
description Fluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power function relationships between the mean and variance of either inter-event intervals or counting statistics, depending on measurement variables. In this article, fluctuation scaling has been formulated for a series of events in which scaling laws in the inter-event intervals and counting statistics were related. We have considered the first-passage time of an Ornstein-Uhlenbeck process and used a conductance-based neuron model with excitatory and inhibitory synaptic inputs to demonstrate the emergence of fluctuation scaling with various exponents, depending on the input regimes and the ratio between excitation and inhibition. Furthermore, we have discussed the possible implication of these results in the context of neural coding.
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institution Kabale University
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record_format Article
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spelling doaj-art-6ca27e22bb904354944906c4c539022d2025-01-24T02:35:23ZengAIMS PressMathematical Biosciences and Engineering1551-00182015-12-0113353755010.3934/mbe.2016006Fluctuation scaling in neural spike trainsShinsuke Koyama0Ryota Kobayashi1Department of Statistical Modeling, The Institute of Statistical Mathematics, 10-3 Midoricho, Tachikawa, TokyoPrinciples of Informatics Research Division, National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda-ku, TokyoFluctuation scaling has been observed universally in a wide variety of phenomena. In time series that describe sequences of events, fluctuation scaling is expressed as power function relationships between the mean and variance of either inter-event intervals or counting statistics, depending on measurement variables. In this article, fluctuation scaling has been formulated for a series of events in which scaling laws in the inter-event intervals and counting statistics were related. We have considered the first-passage time of an Ornstein-Uhlenbeck process and used a conductance-based neuron model with excitatory and inhibitory synaptic inputs to demonstrate the emergence of fluctuation scaling with various exponents, depending on the input regimes and the ratio between excitation and inhibition. Furthermore, we have discussed the possible implication of these results in the context of neural coding.https://www.aimspress.com/article/doi/10.3934/mbe.2016006single neuron models.first-passage timeornstein-uhlenbeck processspike train statisticsneural coding
spellingShingle Shinsuke Koyama
Ryota Kobayashi
Fluctuation scaling in neural spike trains
Mathematical Biosciences and Engineering
single neuron models.
first-passage time
ornstein-uhlenbeck process
spike train statistics
neural coding
title Fluctuation scaling in neural spike trains
title_full Fluctuation scaling in neural spike trains
title_fullStr Fluctuation scaling in neural spike trains
title_full_unstemmed Fluctuation scaling in neural spike trains
title_short Fluctuation scaling in neural spike trains
title_sort fluctuation scaling in neural spike trains
topic single neuron models.
first-passage time
ornstein-uhlenbeck process
spike train statistics
neural coding
url https://www.aimspress.com/article/doi/10.3934/mbe.2016006
work_keys_str_mv AT shinsukekoyama fluctuationscalinginneuralspiketrains
AT ryotakobayashi fluctuationscalinginneuralspiketrains