Synchronization in Networks With Heterogeneous Adaptation Rules and Applications to Distance-Dependent Synaptic Plasticity

Berner, Rico; Yanchuk, Serhiy

doi:10.3389/fams.2021.714978

Cited by 8 publications

(8 citation statements)

References 91 publications

(113 reference statements)

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“…Heterogeneity in the adaptation rule has been known to exist e.g. in neural systems for a long time [53] but is rarely used in modeling [21,22]. Our findings contribute to the important question how heterogeneity may change the dynamics of complex networks.…”

Section: Discussionmentioning

confidence: 68%

Asymmetric Adaptivity induces Recurrent Synchronization in Complex Networks

Thiele¹,

Berner²,

Tass³

et al. 2021

Preprint

Self Cite

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Rhythmic activities that alternate between coherent and incoherent phases are ubiquitous in chemical, ecological, climate, or neural systems. Despite their importance, general mechanisms for their emergence are little understood. In order to fill this gap, we present a framework for describing the emergence of recurrent synchronization in complex networks with adaptive interactions. This phenomenon is manifested at the macroscopic level by temporal episodes of coherent and incoherent dynamics that alternate recurrently. At the same time, the dynamics of the individual nodes do not change qualitatively. We identify asymmetric adaptation rules and temporal separation between the adaptation and the dynamics of individual nodes as key ingredients for the emergence of recurrent synchronization. Our results suggest that asymmetric adaptation might play a fundamental role for pattern generators, e.g., in neuronal systems.

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Section: Discussionmentioning

confidence: 68%

Asymmetric Adaptivity induces Recurrent Synchronization in Complex Networks

Thiele¹,

Berner²,

Tass³

et al. 2021

Preprint

Self Cite

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show abstract

“…Given a post-synaptic neuron i and assuming that it receives inputs from N pre-synaptic neurons j , its membrane potential is described by the following equation: where ϵ ij is the synaptic weight of the connection between the pre-synaptic neuron j and the post-synaptic neuron i , with the dimension of a conductance. In this work, we assumed that each electrical synapse has a weight inversely proportional to the distance between the two connected neurons [52]. It has been observed that the structure of neuronal networks of both brain areas and cellular monolayers can be described by Small-World (SW) graphs [40,43,53].…”

Section: Outline Of the Computational Platformmentioning

confidence: 99%

“…(4) where 𝜖 𝑖𝑗 is the synaptic weight of the connection between the pre-synaptic neuron 𝑗 and the postsynaptic neuron 𝑖, with the dimension of a conductance. In this work, we assumed that each electrical synapse has a weight inversely proportional to the distance between the two connected neurons [52].…”

Section: Connectivity Modelmentioning

confidence: 99%

Digitoids: a novel computational platform for mimicking oxygen-dependent firing dynamics inin vitroneuronal networks

Fabbri,

Ahluwalia,

Magliaro

2023

Preprint

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In vitromodels of neural tissues are crucial to gain new insights on the pathophysiology of the brain. However, cell cultures are associated with many drawbacks and difficulties, e.g., technical complexities, ethical problems and high cost. Computational model-based solutions could represent an important tool to support the study of neuron function. In this work we present a novel computational platform where digital neuronal networks, i.e., Digitoids, can be developed with different size and layouts. The Digitoids rely on a novel firing model where the dependence on oxygen concentration is introduced, since it is a crucial limiting factor in cell cultures. To validate the performance of the platform, Digitoids were developed with the same morphological arrangement as observed in neuron monolayersin vitro. The comparison between the functional output of the Digitoids and the experimental data are not statistically different. The platform delivers a flexible digital tool that can easily be adapted for mimickingin vitromodels with increasing complexity and can be exploited to optimize the laboratory experiments involving neuron cultures.Author SummaryThe use of cell models within laboratories is crucial to gain new understandings of the functioning of neuronal assemblies. However, culturing cells requires highly skilled personnel, a huge quantity of disposable materials and is thus associated with elevated costs. To overcome these limitations, the use of computer-based systems that reproduce the electrical behaviour of neurons can be employed. Sincein vitromodels are vessel-free, we present a novel computational model of neuron electrophysiology, where we introduce the dependence on local oxygen concentration Indeed, oxygen affects the metabolism and the function of cultured neurons. Thanks to our model and platform, we are able to reproduce the morphology of the cultured networks of neurons and build the so-calledDigitoids. We then simulate theDigitoidsand obtain their electrophysiological output. We compared theDigitoids’output with experimental data from neurons cultured on microelectrode arrays. We did not find any differences between the electrophysiological output of theDigitoidsand the experimental data, therefore we conclude that our novel oxygen-dependent model can be used to develop more physiologically relevant tools for simulating the activity of cultured neurons.

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“…20,21 So far, little is known about the dynamical effects induced by heterogeneous adaptation. 22,23 The question arises as to which extent heterogeneous adaptation produces new functionality and, thus, represents Chaos ARTICLE scitation.org/journal/cha an important element for system behavior. In this work, we answer this question by showing that heterogeneous adaptation can be a determining ingredient for producing new collective network function.…”

Section: Introductionmentioning

confidence: 99%

Asymmetric adaptivity induces recurrent synchronization in complex networks

Thiele

Berner

Tass

et al. 2023

Chaos: An Interdisciplinary Journal of Nonlinear Science

Self Cite

View full text Add to dashboard Cite

Rhythmic activities that alternate between coherent and incoherent phases are ubiquitous in chemical, ecological, climate, or neural systems. Despite their importance, general mechanisms for their emergence are little understood. In order to fill this gap, we present a framework for describing the emergence of recurrent synchronization in complex networks with adaptive interactions. This phenomenon is manifested at the macroscopic level by temporal episodes of coherent and incoherent dynamics that alternate recurrently. At the same time, the dynamics of the individual nodes do not change qualitatively. We identify asymmetric adaptation rules and temporal separation between the adaptation and the dynamics of individual nodes as key features for the emergence of recurrent synchronization. Our results suggest that asymmetric adaptation might be a fundamental ingredient for recurrent synchronization phenomena as seen in pattern generators, e.g., in neuronal systems.

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Synchronization in Networks With Heterogeneous Adaptation Rules and Applications to Distance-Dependent Synaptic Plasticity

Cited by 8 publications

References 91 publications

Asymmetric Adaptivity induces Recurrent Synchronization in Complex Networks

Asymmetric Adaptivity induces Recurrent Synchronization in Complex Networks

Digitoids: a novel computational platform for mimicking oxygen-dependent firing dynamics inin vitroneuronal networks

Asymmetric adaptivity induces recurrent synchronization in complex networks

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