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.

中文翻译：

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通过在目标导向的达成任务中结合 RAT 主要运动和无粒皮质整体活动的校准反馈范例来增强对前肢运动轨迹的预测。

完整的到达动作涉及目标感知、运动规划和手臂动作执行，这个过程需要各个脑区的整合和沟通。此前，伸手运动已成功从运动皮层 (M1) 解码并应用于假肢控制。然而，大多数研究试图解码单个大脑区域的神经活动，导致视觉引导的到达动作期间解码准确性降低。为了提高视觉引导前肢伸展运动的解码准确性，我们提出了一种并行计算神经网络，使用大鼠的 M1 和内侧无颗粒皮层 (AGm) 神经活动来预测前肢伸展运动。所提出的网络将 M1 神经活动解码为前肢运动的主要组成部分，并将 AGm 神经活动解码为内部前馈信息，以校准目标到达运动中的前肢运动。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着提高解码性能。我们还表明，M1 和 AGm 神经活动有助于在到达目标的运动过程中控制前肢运动，并且我们报告说，随着目标距离，这可能涉及感觉运动转换以及视觉皮层和 AGm 之间的通信，为即将到来的到达运动做准备。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准确性。