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Loss of synchronization in complex neuronal networks with delay

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Judith Lehnert, Thomas Dahms, Philipp Hövel, Eckehard Schöll

Disordered Systems and Neural Networks · Submitted 21 Jul 2011

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Superimposing inhibitory links randomly on top of a regular ring of excitatory coupling, which yields a small-world-like network topology, we find a phase transition to desynchronization as the probability of inhibitory links exceeds a critical value.

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arXiv ID1107.4195 (version 2)
Pith ID34QUGFBV, Pith's permanent record of this paper and its review
CitationEckehard Sch\"oll, Judith Lehnert, Philipp H\"ovel, Thomas Dahms. "Loss of synchronization in complex neuronal networks with delay." arXiv:1107.4195 (2011). https://pith.science/paper/34QUGFBV
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,242 input tokens, $0.0002 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.13 · human participants 0.02 · controlled comparison 0.68 · randomized assignment 0.04 · states quantitative result 0.18 · compares with prior results 0.81 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We investigate the stability of synchronization in networks of delay-coupled excitable neural oscillators. On the basis of the master stability function formalism, we demonstrate that synchronization is always stable for excitatory coupling independently of the delay and coupling strength. Superimposing inhibitory links randomly on top of a regular ring of excitatory coupling, which yields a small-world-like network topology, we find a phase transition to desynchronization as the probability of inhibitory links exceeds a critical value. We explore the scaling of the critical value in dependence on network properties. Compared to random networks, we find that small-world topologies are more susceptible to desynchronization via inhibition.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We investigate the stability of synchronization in networks of delay-coupled excitable neural oscillators. contribution 0.87 0.84 0.04 unclear 0.72
s1 On the basis of the master stability function formalism, we demonstrate that synchronization is always stable for excitatory coupling independently of the delay and coupling strength. result 1.00 0.92 0.29 asserted 1.00
s2 Superimposing inhibitory links randomly on top of a regular ring of excitatory coupling, which yields a small-world-like network topology, we find a phase transition to desynchronization as the probability of inhibitory links exceeds a critical value. result 0.99 0.96 0.55 asserted 1.00
s3 We explore the scaling of the critical value in dependence on network properties. contribution 0.95 0.88 0.04 unclear 0.74
s4 Compared to random networks, we find that small-world topologies are more susceptible to desynchronization via inhibition. result 0.99 0.95 0.21 asserted 1.00

What kind of paper the classifier reports this is: theoretical result 0.97 · evidence from proof or derivation 0.76.

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  • references and citing sentences
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  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We investigate the stability of synchronization in networks of delay-coupled excitable neural oscillators. On the basis of the master stability function formalism, we demonstrate that synchronization is always stable for excitatory coupling independently of the delay and coupling strength. Superimposing inhibitory links randomly on top of a regular ring of excitatory coupling, which yields a small-world-like network topology, we find a phase transition to desynchronization as the probability of inhibitory links exceeds a critical value. We explore the scaling of the critical value in dependence on network properties. Compared to random networks, we find that small-world topologies are more susceptible to desynchronization via inhibition.

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