激励-抑制平衡控制耦合相位振荡器的简单模型同步。

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Satoshi Kuroki, Kenji Mizuseki
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引用次数: 0

摘要

大脑中的集体神经元活动在休息时同步,在活动时不同步,影响记忆巩固、知识抽象和创造性思维等认知过程。这些状态涉及抑制的显著调节,这改变了突触输入的兴奋-抑制(EI)平衡。然而,EI平衡对集体神经元振荡的影响尚不完全清楚。在本研究中,我们引入了EI-Kuramoto模型,这是Kuramoto模型的改进版本,其中振荡子被分为兴奋性和抑制性组,具有四种不同的相互作用类型:兴奋-兴奋、兴奋-抑制、抑制-兴奋和抑制-抑制。数值模拟确定了同步、双稳态和非同步三种动态状态,这三种动态状态可以通过调整四种相互作用类型的强度来控制。理论分析进一步表明,这些相互作用之间的平衡在决定动态状态方面起着关键作用。这项研究为EI平衡在同步耦合振荡器和神经元中的作用提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Excitation-Inhibition Balance Controls Synchronization in a Simple Model of Coupled Phase Oscillators.

Collective neuronal activity in the brain synchronizes during rest and desynchronizes during active behaviors, influencing cognitive processes such as memory consolidation, knowledge abstraction, and creative thinking. These states involve significant modulation of inhibition, which alters the excitation-inhibition (EI) balance of synaptic inputs. However, the influence of the EI balance on collective neuronal oscillation remains only partially understood. In this study, we introduce the EI-Kuramoto model, a modified version of the Kuramoto model, in which oscillators are categorized into excitatory and inhibitory groups with four distinct interaction types: excitatory-excitatory, excitatory-inhibitory, inhibitory-excitatory, and inhibitory-inhibitory. Numerical simulations identify three dynamic states-synchronized, bistable, and desynchronized-that can be controlled by adjusting the strength of the four interaction types. Theoretical analysis further demonstrates that the balance among these interactions plays a critical role in determining the dynamic states. This study provides valuable insights into the role of EI balance in synchronizing coupled oscillators and neurons.

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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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