Generalising the Kuramoto model for the study of neuronal synchronisation in the brain

Cumin, David; Unsworth, Charles P.

doi:10.1016/j.physd.2006.12.004

Cited by 199 publications

(180 citation statements)

References 13 publications

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“…Starting with the network model, the original formulation of the Kuramoto Model doesn't take into account important properties of real nervous systems (e.g. spatial distribution of units, transmission delays, asymmetrical interactions), therefore an important step towards a more biologically plausible architectures would be to implement extended versions of the model which tackle some of these constraints (Tass, 2006;Cumin & Unsworth, 2007;Breakspear et al, 2010). These extensions may also unfold more complex, metastable dynamics which are intrinsically connected to dynamic pattern formation in brain activity and hence are fundamental to adaptive behaviour (Omelćhenko et al, 2008;Tognoli & Kelso, 2009;Chialvo, 2010;Shanahan, 2010).…”

Section: Discussionmentioning

confidence: 99%

“…The natural frequency w i can be associated with the natural firing rate of a neuron or a group of neurons, and the sensory inputs mediated by z i alters its oscillatory behaviour according to environmental interactions, thus improving the flexibility of the model to study neuronal synchronisation (Cumin & Unsworth, 2007) within a behavioural context.…”

Section: Framework For Application In Evolutionary Roboticsmentioning

confidence: 99%

“…The neuronal model employed is based on the Kuramoto Model of coupled phase oscillators (Kuramoto, 1984), which has been extensively studied in the Statistical Physics literature, with recent applications in a biological context due to its relatively simple and abstract mathematical formulation yet complex activity that can be exploited to clarify fundamental mechanisms of neuro-oscillatory phenomena without making too many a priori assumptions (Ermentrout & Kleinfeld, 2001;Cumin & Unsworth, 2007;Kitzbichler et al, 2009;Breakspear et al, 2010). The model explicitly captures the phase dynamics of units that alone have spontaneous oscillatory activity and once connected can generate emergent rhythmic patterns.…”

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Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

2012

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Article (Accepted Version) http://sro.sussex.ac.uk Moioli, Renan C, Vargas, Patricia A and Husbands, Phil (2012) Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent. Biological Cybernetics, 106 (6-7). pp. 407-427. ISSN 0340-1200 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/40960/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. Noname manuscript No. (will be inserted by the editor)Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent Renan C. Moioli, Patricia A. Vargas, Phil Husbands Abstract Oscillatory activity is ubiquitous in nervous systems, with solid evidence that synchronisation mechanisms underpin cognitive processes. Nevertheless, its informational content and relationship with behaviour are still to be fully understood. Additionally, cognitive systems cannot be properly appreciated without taking into account brain -body -environment interactions. In this paper, we developed a model based on the Kuramoto Model of coupled phase oscillators to explore the role of neural synchronisation in the performance of a simulated robotic agent in two different minimally cognitive tasks. We show that there is a statistically significant difference in performance and evolvability depending on the synchronisation regime of the network. In both tasks, a combination of information flow and dynamical analyses show that networks with a definite, but not too strong, propensity for synchronisation are more able to reconfigure, to organise themselves functionally and to adapt to different behavioural conditions. The results highlight the asymmetry of information flow and its behavioural correspondence. Importantly it also shows that neural synchronisation dynamics, when suitably flexible and reconfigurable, can generate minimally cognitive embodied behaviour.

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Section: Discussionmentioning

confidence: 99%

Section: Framework For Application In Evolutionary Roboticsmentioning

confidence: 99%

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confidence: 99%

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Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

2012

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“…Thus, each oscillator of the Kuromoto model can be seen as corresponding to the natural firing rate of a neuron. In order to make this model more realistic, different groups adapted the model and developed more sophisticated versions to provide different coupling strengths and time varying parameters [67].…”

Section: A31 Macroscopic Modelsmentioning

confidence: 99%

“…As highlighted by a number of researchers [113,67,114], it was Winfree [115] who, in order to explain the observed behaviour of populations of biological oscillators, formulated a model consisting of a system of nearly identical and coupled limit cycle oscillators. Winfree [115] went on to prove that the system behaved incoherently or synchronised depending on the degree of coupling; namely, incoherent when the coupling was small compared with the spread of the frequencies of the oscillators, partial synchronisation when the coupling exceeded a threshold and fully locked phase and amplitude coupling for strong coupling, as illustrated by Strogatz [113, Figure 2].…”

Section: F1 the Phenomenon Of Synchronisationmentioning

confidence: 99%

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Dunn¹,

Abbott²,

Masterton³

et al. 2011

ANZIAMJ

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The Brain Research Institute (bri) uses various types of indirect measurements, including eeg and fmri, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the bri asked the misg 2010 participants to examine how the available eeg and fmri data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was

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Analytical and experimental study for mechanical vibrations of a two‐coupled spring masses system via Caputo‐based derivatives

Jiménez

Cruz–Duarte

Escalante-Martínez

et al. 2021

Math Methods in App Sciences

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This work studies a mechanical system composed of two‐spring coupled masses arrangement without damping using fractional derivatives under the Caputo sense. We determine and implement an explicit model based on the Caputo‐Fabrizio operator. To achieve this model, we detail a systematic methodology that avoids dimensional inconsistencies. Then, we use the proposed model to explain the system dynamic acquired from an experimental (quite rudimentary) setup. We also compare our results to those achieved from the ordinary model and the numerical implementation using the Caputo derivative definition. Results prove that the proposed model describes the dynamic of this mechanical system better than the ordinary and numerical models. We also show that the fractional order allows the model (obtained from an idealized undamped system) to describe dynamics from fully dissipative to wholly oscillatory. Moreover, we discuss other exciting characteristics of the obtained responses.

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Generalising the Kuramoto model for the study of neuronal synchronisation in the brain

Cited by 199 publications

References 13 publications

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Synchronisation effects on the behavioural performance and information dynamics of a simulated minimally cognitive robotic agent

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Analytical and experimental study for mechanical vibrations of a two‐coupled spring masses system via Caputo‐based derivatives

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