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Regular spiking in high-conductance states: The essential role of inhibition

SYSNO ASEP	0541638
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Článek ve WOS
Title	Regular spiking in high-conductance states: The essential role of inhibition
Author(s)	Bárta, Tomáš (FGU-C)_{RID, ORCID} Košťál, Lubomír (FGU-C)_{RID, ORCID, SAI}
Article number	022408
Source Title	Physical Review E. - : American Physical Society - ISSN 2470-0045 Roč. 103, č. 2 (2021)
Number of pages	13 s.
Language	eng - English
Country	US - United States
Keywords	inhibition ; synaptic noise ; neuronal models ; spike-firing adaptation ; leaky integrate-and-fire ; Hodgkin-Huxley ; neuron
Subject RIV	EA - Cell Biology
OECD category	Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology
R&D Projects	GA20-10251S GA ČR - Czech Science Foundation (CSF)
Method of publishing	Limited access
Institutional support	FGU-C - RVO:67985823
UT WOS	000619236600004
EID SCOPUS	85101275184
DOI	10.1103/PhysRevE.103.022408
Annotation	Strong inhibitory input to neurons, which occurs in balanced states of neural networks, increases synaptic current fluctuations. This has led to the assumption that inhibition contributes to the high spike-firing irregularity observed in vivo. We used single compartment neuronal models with time-correlated (due to synaptic filtering) and state-dependent (due to reversal potentials) input to demonstrate that inhibitory input acts to decrease membrane potential fluctuations, a result that cannot be achieved with simplified neural input models. To clarify the effects on spike-firing regularity, we used models with different spike-firing adaptation mechanisms, and we observed that the addition of inhibition increased firing regularity in models with dynamic firing thresholds and decreased firing regularity if spike-firing adaptation was implemented through ionic currents or not at all. This fluctuation-stabilization mechanism provides an alternative perspective on the importance of strong inhibitory inputs observed in balanced states of neural networks, and it highlights the key roles of biologically plausible inputs and specific adaptation mechanisms in neuronal modeling.
Workplace	Institute of Physiology
Contact	Lucie Trajhanová, lucie.trajhanova@fgu.cas.cz, Tel.: 241 062 400
Year of Publishing	2022
Electronic address	https://doi.org/10.1103/PhysRevE.103.022408

Number of the records: 1