Open AccessFluctuation scaling in neural spike trains
Author(s) -
Shinsuke Koyama,
Ryota Kobayashi
Publication year - 2016
Publication title -
mathematical biosciences and engineering
Language(s) - Uncategorized
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2016006
Subject(s) - scaling , statistical physics , context (archaeology) , neural coding , series (stratigraphy) , spike train , first hitting time model , ornstein–uhlenbeck process , mathematics , stochastic process , event (particle physics) , statistics , spike (software development) , physics , computer science , artificial intelligence , paleontology , geometry , software engineering , quantum mechanics , biology
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.