Počet záznamů: 1
Regular spiking in high-conductance states: The essential role of inhibition
- 1.0541638 - FGÚ 2022 RIV US eng J - Článek v odborném periodiku
Bárta, Tomáš - Košťál, Lubomír
Regular spiking in high-conductance states: The essential role of inhibition.
Physical Review E. Roč. 103, č. 2 (2021), č. článku 022408. ISSN 2470-0045. E-ISSN 2470-0053
Grant CEP: GA ČR(CZ) GA20-10251S
Institucionální podpora: RVO:67985823
Klíčová slova: inhibition * synaptic noise * neuronal models * spike-firing adaptation * leaky integrate-and-fire * Hodgkin-Huxley * neuron
Obor OECD: Biology (theoretical, mathematical, thermal, cryobiology, biological rhythm), Evolutionary biology
Impakt faktor: 2.707, rok: 2021 ; AIS: 0.733, rok: 2021
Způsob publikování: Omezený přístup
Web výsledku:
https://doi.org/10.1103/PhysRevE.103.022408DOI: https://doi.org/10.1103/PhysRevE.103.022408
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.
Trvalý link: http://hdl.handle.net/11104/0319169Název souboru Staženo Velikost Komentář Verze Přístup 21_0017_0541638.pdf 0 4.5 MB Vydavatelský postprint vyžádat
Počet záznamů: 1