Evidence map›Paper›PMID 34853889›Full record

ReviewBiological cybernetics2022

Phase response approaches to neural activity models with distributed delay.

Marius Winkler, Grégory Dumont, Eckehard Schöll, Boris Gutkin

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In one paragraph

Review in Biological cybernetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Marius WinklerGroup for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France.ORCID 0000-0002-1280-292X
Grégory DumontGroup for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France.
Eckehard SchöllInstitut für Theoretische Physik, Technische Universität Berlin, Hardenbergstrasse 36, 10623, Berlin, Germany.ORCID 0000-0002-7318-2672
Boris GutkinGroup for Neural Theory, LNC INSERM U960, DEC, Ecole Normale Supérieure PSL* University, 24 rue Lhomond, 75005, Paris, France. boris.gutkin@ens.fr.ORCID 0000-0001-6409-979X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In weakly coupled neural oscillator networks describing brain dynamics, the coupling delay is often distributed. We present a theoretical framework to calculate the phase response curve of distributed-delay induced limit cycles with infinite-dimensional phase space. Extending previous works, in which non-delayed or discrete-delay systems were investigated, we develop analytical results for phase response curves of oscillatory systems with distributed delay using Gaussian and log-normal delay distributions. We determine the scalar product and normalization condition for the linearized adjoint of the system required for the calculation of the phase response curve. As a paradigmatic example, we apply our technique to the Wilson-Cowan oscillator model of excitatory and inhibitory neuronal populations under the two delay distributions. We calculate and compare the phase response curves for the Gaussian and log-normal delay distributions. The phase response curves obtained from our adjoint calculations match those compiled by the direct perturbation method, thereby proving that the theory of weakly coupled oscillators can be applied successfully for distributed-delay-induced limit cycles. We further use the obtained phase response curves to derive phase interaction functions and determine the possible phase locked states of multiple inter-coupled populations to illuminate different synchronization scenarios. In numerical simulations, we show that the coupling delay distribution can impact the stability of the synchronization between inter-coupled gamma-oscillatory networks.

Indexed as

Neural Networks, ComputerNeuronsBrainCoupled oscillatorsDistributed delayPhase response curveWilson–Cowan model

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.