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Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks

Research output: Contribution to Journal/Magazine › Journal article › peer-review

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Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks. / Devalle, Federico; Roxin, Alex; Montbrió, Ernest.
In: PLoS Computational Biology, Vol. 13, No. 12, 1005881, 29.12.2017.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Devalle, F, Roxin, A & Montbrió, E 2017, 'Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks', PLoS Computational Biology, vol. 13, no. 12, 1005881. https://doi.org/10.1371/journal.pcbi.1005881

APA

Devalle, F., Roxin, A., & Montbrió, E. (2017). Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks. PLoS Computational Biology, 13(12), Article 1005881. https://doi.org/10.1371/journal.pcbi.1005881

Vancouver

Devalle F, Roxin A, Montbrió E. Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks. PLoS Computational Biology. 2017 Dec 29;13(12):1005881. doi: 10.1371/journal.pcbi.1005881

Author

Devalle, Federico ; Roxin, Alex ; Montbrió, Ernest. / Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks. In: PLoS Computational Biology. 2017 ; Vol. 13, No. 12.

Bibtex

@article{b6a30b25dbf44444af2127f1e88c6c14,
title = "Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks",
abstract = "Author summary Population models describing the average activity of large neuronal ensembles are a powerful mathematical tool to investigate the principles underlying cooperative function of large neuronal systems. However, these models do not properly describe the phenomenon of spike synchrony in networks of neurons. In particular, they fail to capture the onset of synchronous oscillations in networks of inhibitory neurons. We show that this limitation is due to a voltage-dependent synchronization mechanism which is naturally present in spiking neuron models but not captured by traditional firing rate equations. Here we investigate a novel set of macroscopic equations which incorporate both firing rate and membrane potential dynamics, and that correctly generate fast inhibition-based synchronous oscillations. In the limit of slow-synaptic processing oscillations are suppressed, and the model reduces to an equation formally equivalent to the Wilson-Cowan model.",
author = "Federico Devalle and Alex Roxin and Ernest Montbri{\'o}",
year = "2017",
month = dec,
day = "29",
doi = "10.1371/journal.pcbi.1005881",
language = "English",
volume = "13",
journal = "PLoS Computational Biology",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "12",

}

RIS

TY - JOUR

T1 - Firing rate equations require a spike synchrony mechanism to correctly describe fast oscillations in inhibitory networks

AU - Devalle, Federico

AU - Roxin, Alex

AU - Montbrió, Ernest

PY - 2017/12/29

Y1 - 2017/12/29

N2 - Author summary Population models describing the average activity of large neuronal ensembles are a powerful mathematical tool to investigate the principles underlying cooperative function of large neuronal systems. However, these models do not properly describe the phenomenon of spike synchrony in networks of neurons. In particular, they fail to capture the onset of synchronous oscillations in networks of inhibitory neurons. We show that this limitation is due to a voltage-dependent synchronization mechanism which is naturally present in spiking neuron models but not captured by traditional firing rate equations. Here we investigate a novel set of macroscopic equations which incorporate both firing rate and membrane potential dynamics, and that correctly generate fast inhibition-based synchronous oscillations. In the limit of slow-synaptic processing oscillations are suppressed, and the model reduces to an equation formally equivalent to the Wilson-Cowan model.

AB - Author summary Population models describing the average activity of large neuronal ensembles are a powerful mathematical tool to investigate the principles underlying cooperative function of large neuronal systems. However, these models do not properly describe the phenomenon of spike synchrony in networks of neurons. In particular, they fail to capture the onset of synchronous oscillations in networks of inhibitory neurons. We show that this limitation is due to a voltage-dependent synchronization mechanism which is naturally present in spiking neuron models but not captured by traditional firing rate equations. Here we investigate a novel set of macroscopic equations which incorporate both firing rate and membrane potential dynamics, and that correctly generate fast inhibition-based synchronous oscillations. In the limit of slow-synaptic processing oscillations are suppressed, and the model reduces to an equation formally equivalent to the Wilson-Cowan model.

U2 - 10.1371/journal.pcbi.1005881

DO - 10.1371/journal.pcbi.1005881

M3 - Journal article

VL - 13

JO - PLoS Computational Biology

JF - PLoS Computational Biology

SN - 1553-734X

IS - 12

M1 - 1005881

ER -