Li Suchen, Tang Zhuo, Li Mengmeng, Yang Lifang, Shang Zhigang
School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China.
Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology, Zhengzhou 450001, China.
Animals (Basel). 2025 Jun 23;15(13):1851. doi: 10.3390/ani15131851.
Flight behavior in pigeons is governed by intricate neural mechanisms that regulate movement patterns and flight dynamics. Among various kinematic parameters, flight acceleration provides critical information for the brain to modulate movement intensity, speed, and direction. However, the neural representation mechanisms underlying flight acceleration remain insufficiently understood. To address this, we conducted outdoor free-flight experiments in homing pigeons, during which GPS data, flight posture, and eight-channel local field potentials (LFPs) were synchronously recorded. Our analysis revealed that gamma-band activity in the dorsal intermediate arcopallium (AId) region was more prominent during behaviorally demanding phases of flight. In parallel, local functional network analysis showed that the clustering coefficient of gamma-band activity in the AId followed a nonlinear, U-shaped relationship with flight acceleration-exhibiting the strongest and most widespread connectivity during deceleration, moderate connectivity during acceleration, and the weakest network coupling during steady flight. This pattern likely reflects the increased neural demands associated with flight phase transitions, where greater cognitive and sensorimotor integration is required. Furthermore, using LFP signals from five distinct frequency bands as input, machine learning models were developed to decode flight acceleration, further confirming the role of gamma-band dynamics in motor regulation during natural flight. This study provides the first evidence that gamma-band activity in the avian AId region encodes flight acceleration, offering new insights into the neural representation of motor states in natural flight and implications for bio-inspired flight control systems.
鸽子的飞行行为受复杂的神经机制支配，这些机制调节运动模式和飞行动力学。在各种运动学参数中，飞行加速度为大脑调节运动强度、速度和方向提供关键信息。然而，飞行加速度背后的神经表征机制仍未得到充分理解。为了解决这个问题，我们对归巢鸽进行了户外自由飞行实验，在此期间同步记录了GPS数据、飞行姿态和八通道局部场电位（LFP）。我们的分析表明，在飞行中对行为要求较高的阶段，背侧中间古皮质（AId）区域的伽马波段活动更为突出。同时，局部功能网络分析表明，AId中伽马波段活动的聚类系数与飞行加速度呈非线性U形关系，在减速期间表现出最强和最广泛的连通性，在加速期间连通性适中，在稳定飞行期间网络耦合最弱。这种模式可能反映了与飞行阶段转换相关的神经需求增加，其中需要更大的认知和感觉运动整合。此外，利用来自五个不同频段的LFP信号作为输入，开发了机器学习模型来解码飞行加速度，进一步证实了伽马波段动力学在自然飞行中的运动调节作用。这项研究首次证明鸟类AId区域的伽马波段活动编码飞行加速度，为自然飞行中运动状态的神经表征提供了新的见解，并对仿生飞行控制系统具有启示意义。
