基于异质吸引子模型的运动制备动力学机制揭示

IF 5.7 1区 数学 Q1 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS Chaos Solitons & Fractals Pub Date : 2025-05-01 Epub Date: 2025-02-28 DOI:10.1016/j.chaos.2025.116220
Xiaomeng Wang , Lining Yin , Ying Yu , Qingyun Wang
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引用次数: 0

摘要

不同类型的抑制性中间神经元在调节自主运动的反应时间和准确性方面起着至关重要的作用。为了探索这种调节背后的神经动力学机制,我们构建了一个皮层抑制性微回路模型,包括兴奋性准备核和执行核以及三个抑制性中间神经元核。它复制了生理实验中观察到的运动皮层在运动准备和执行过程中的神经活动模式,以及由学习引起的活动变化。我们分析了抑制性突触强度和抑制性神经元自放电率对模型自主运动的影响。我们的研究结果表明,生长抑素(SST)和小白蛋白(PV)神经元在锥体细胞(PC)上的抑制突触强度可以显著影响反应时间。SST和PV的抑制作用比例可以通过调节它们的放电速率来提高反应速度和运动选择的准确性。进一步的分析表明,海温优势选择导致更快但更不准确的反应,而pv优势选择产生更慢但更精确的结果。最后,利用平均场方法,我们发现系统在准备和执行阶段的稳定点和吸引域是不同的。学习扩大了准备和执行阶段选择正确的吸引域,选择失败的平衡点消失,提高了反应速度和正确选择的率。我们的模型为自主运动控制和学习中的抑制网络动力学提供了新的见解。
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Unraveling the dynamical mechanisms of motor preparation based on the heterogeneous attractor model
Different types of inhibitory interneurons play a crucial role in regulating the reaction time and accuracy of voluntary movements. To explore the neurodynamic mechanisms underlying this regulation, we construct a cortical inhibitory microcircuit model, comprising excitatory preparatory and executive nuclei as well as three inhibitory interneuron nuclei. It replicates the neural activity patterns in the motor cortex during movement preparation and execution observed in physiological experiments, as along with activity changes induced by learning. We analyze the effects of inhibitory synaptic strength and inhibitory neuron self-firing rate on voluntary movement in the model. Our findings reveal that the inhibitory synaptic strength of somatostatin (SST) and parvalbumin (PV) neurons on pyramidal cells (PC) can significantly affect reaction time. By regulating their firing rates, the ratio of the inhibitory effects of SST and PV can improve the response speed and the accuracy of motion selection. Further analysis indicates that SST-dominated selection leads to quicker but less accurate responses, whereas PV-dominated selection produces slower but more precise outcomes. Finally, using a mean-field approach, we find that the stabilization points and attraction domains of the system are different in the preparation and execution phase. Learning expands the attraction domain for choosing correctly in the preparation and execution phases and the equilibrium point for choosing failures disappears, improving the speed of reaction and the rate of correct choices. Our model gives new insights into the dynamics of inhibitory networks in voluntary movement control and learning.
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来源期刊
Chaos Solitons & Fractals
Chaos Solitons & Fractals 物理-数学跨学科应用
CiteScore
13.20
自引率
10.30%
发文量
1087
审稿时长
9 months
期刊介绍: Chaos, Solitons & Fractals strives to establish itself as a premier journal in the interdisciplinary realm of Nonlinear Science, Non-equilibrium, and Complex Phenomena. It welcomes submissions covering a broad spectrum of topics within this field, including dynamics, non-equilibrium processes in physics, chemistry, and geophysics, complex matter and networks, mathematical models, computational biology, applications to quantum and mesoscopic phenomena, fluctuations and random processes, self-organization, and social phenomena.
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