生理纺锤体模型与脊髓通路的闭环耦合对人体中心伸出的感觉运动控制。

IF 2.3 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Frontiers in Computational Neuroscience Pub Date : 2025-08-26 eCollection Date: 2025-01-01 DOI:10.3389/fncom.2025.1575630
Pablo Filipe Santana Chacon, Isabell Wochner, Maria Hammer, Jochen Martin Eppler, Susanne Kunkel, Syn Schmitt
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引用次数: 0

摘要

考虑层次感觉运动控制系统的不同结构的新研究的发展对于实现对运动的更全面的理解是必不可少的。将更多的生物本体感觉和神经回路模型结合到肌肉中可以使神经肌肉骨骼系统更适合于研究和阐明运动控制。具体来说,考虑本体感觉和中枢神经系统之间闭环的进一步研究可能会更好地理解传入反馈在完整生物系统中对感觉运动学习和执行的重要性这一尚未解决的问题。因此,本研究旨在探讨针刺神经元网络对纺锤体传入放电的处理及其与感觉运动控制的相关性。我们将先前发表的肌肉纺锤体生理模型整合到生物手臂模型中,该模型对应于能够在demoa多体模拟框架内复制生物运动的肌肉骨骼系统。我们将这种肌肉骨骼系统与生理驱动的神经脊髓通路结合起来,这是基于NEST脉冲神经网络模拟器的文献实现的,旨在执行由脊髓突触学习引起的人类中心向外到达。结果,纺锤体与脊髓神经元的连接在更困难的目标(即高于放置的目标)受到扰动时得到加强,突出了纺锤体本体感觉在更困难的情况下取得成功的重要性。此外,一个额外实现的更简单的脊髓网络(不包括具有纺锤体本体感觉的通路)在任务中表现较差,因为无法到达所有评估的目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Closed-loop coupling of both physiological spindle model and spinal pathways for sensorimotor control of human center-out reaching.

The development of new studies that consider different structures of the hierarchical sensorimotor control system is essential to enable a more holistic understanding about movement. The incorporation of more biological proprioceptive and neuronal circuit models to muscles can turn neuromusculoskeletal systems more appropriate to investigate and elucidate motor control. Specifically, further studies that consider the closed-loop between proprioception and central nervous system may allow to better understand the yet open question about the importance of afferent feedback for sensorimotor learning and execution in the intact biological system. Therefore, this study aims to investigate the processing of spindle afferent firings by spiking neuronal network and their relevance for sensorimotor control. We integrated our previously published physiological model of the muscle spindle in a biological arm model, corresponding to a musculoskeletal system able to reproduce biological motion inside of the demoa multi-body simulation framework. We coupled this musculoskeletal system to physiologically-motivated neuronal spinal pathways, which were implemented based on literature in the NEST spiking neural network simulator, intended to perform human center-out reaching arising from spinal synaptic learning. As result, the spindle connections to the spinal neurons were strengthened for the more difficult targets (i.e. higher above placed targets) under perturbation, highlighting the importance of spindle proprioception to succeed in more difficult scenarios. Furthermore, an additionally-implemented simpler spinal network (that does not include the pathways with spindle proprioception) presented an inferior performance in the task by not being able to reach all the evaluated targets.

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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
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