通过在目标定向达成任务中结合RAT初级运动和无核皮层集合活动的校准反馈范式来增强对前臂运动轨迹的预测。

IF 6.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
International Journal of Neural Systems Pub Date : 2023-10-01 Epub Date: 2023-08-24 DOI:10.1142/S012906572350051X
Han-Lin Wang, Yun-Ting Kuo, Yu-Chun Lo, Chao-Hung Kuo, Bo-Wei Chen, Ching-Fu Wang, Zu-Yu Wu, Chi-En Lee, Shih-Hung Yang, Sheng-Huang Lin, Po-Chuan Chen, You-Yin Chen
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引用次数: 0

摘要

完整的伸展运动涉及目标感知、运动规划和手臂运动执行,这一过程需要大脑各个区域的整合和交流。以前,伸展运动已经从运动皮层(M1)成功解码,并应用于假肢控制。然而,大多数研究试图从单个大脑区域解码神经活动,导致在视觉引导的伸手运动中解码精度降低。为了提高视觉引导下前肢伸展运动的解码精度,我们提出了一种并行计算神经网络,利用大鼠的M1和内侧无核皮层(AGm)神经活动来预测前肢伸展动作。所提出的网络将M1神经活动解码为前肢运动的主要成分,并将AGm神经活动解码成内部前馈信息,以校准达到目标的运动中的前肢运动。我们证明,与没有校准的神经解码器相比,使用AGm神经活动来校准M1预测的前肢运动可以显著提高解码性能。我们还表明,M1和AGm神经活动有助于在达到目标的运动过程中控制前肢运动,并且我们报告了随着目标距离的变化,在AGm上β和γ带的局部场电位(LFP)的功率增加,这可能涉及在为即将到来的伸手动作做准备时视觉皮层和AGm之间的感觉运动转换和交流。所提出的具有内部反馈模型的并行计算神经网络提高了目标到达运动的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing Prediction of Forelimb Movement Trajectory through a Calibrating-Feedback Paradigm Incorporating RAT Primary Motor and Agranular Cortical Ensemble Activity in the Goal-Directed Reaching Task.

Complete reaching movements involve target sensing, motor planning, and arm movement execution, and this process requires the integration and communication of various brain regions. Previously, reaching movements have been decoded successfully from the motor cortex (M1) and applied to prosthetic control. However, most studies attempted to decode neural activities from a single brain region, resulting in reduced decoding accuracy during visually guided reaching motions. To enhance the decoding accuracy of visually guided forelimb reaching movements, we propose a parallel computing neural network using both M1 and medial agranular cortex (AGm) neural activities of rats to predict forelimb-reaching movements. The proposed network decodes M1 neural activities into the primary components of the forelimb movement and decodes AGm neural activities into internal feedforward information to calibrate the forelimb movement in a goal-reaching movement. We demonstrate that using AGm neural activity to calibrate M1 predicted forelimb movement can improve decoding performance significantly compared to neural decoders without calibration. We also show that the M1 and AGm neural activities contribute to controlling forelimb movement during goal-reaching movements, and we report an increase in the power of the local field potential (LFP) in beta and gamma bands over AGm in response to a change in the target distance, which may involve sensorimotor transformation and communication between the visual cortex and AGm when preparing for an upcoming reaching movement. The proposed parallel computing neural network with the internal feedback model improves prediction accuracy for goal-reaching movements.

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来源期刊
International Journal of Neural Systems
International Journal of Neural Systems 工程技术-计算机:人工智能
CiteScore
11.30
自引率
28.80%
发文量
116
审稿时长
24 months
期刊介绍: The International Journal of Neural Systems is a monthly, rigorously peer-reviewed transdisciplinary journal focusing on information processing in both natural and artificial neural systems. Special interests include machine learning, computational neuroscience and neurology. The journal prioritizes innovative, high-impact articles spanning multiple fields, including neurosciences and computer science and engineering. It adopts an open-minded approach to this multidisciplinary field, serving as a platform for novel ideas and enhanced understanding of collective and cooperative phenomena in computationally capable systems.
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