Neural signals for locomotion are influenced both by the neural network architecture and sensory inputs coordinating and adapting the gait to the environment. Adaptation relies on the ability to change amplitude, frequency, and phase of the signals within the sensorimotor loop in response to external stimuli. However, in order to experiment with closed-loop control, we first need a better understanding of the dynamics of the system and how adaptation works. Based on insights from biology, we developed a spiking neural network capable of continuously changing amplitude, frequency, and phase online. The resulting network is deployed on a hexapod robot in order to observe the walking behavior. The morphology and parameters of the network results in a tripod gait, demonstrating that a design without afferent feedback is sufficient to maintain a stable gait. This is comparable to results from biology showing that deafferented samples exhibit a tripod-like gait and adds to the evidence for a meaningful role of network topology in locomotion. Further, this work enables research into the role of sensory feedback and high-level control signals in the adaptation of gait types. A better understanding of the neural control of locomotion relates back to biology where it can provide evidence for theories that are currently not testable on live insects.

中文翻译：

---

六脚步行走过程中用于在线操作的灵活钉刺CPG。

神经运动的神经信号受神经网络架构和协调和适应环境步态的感觉输入的影响。适应依赖于响应外部刺激而改变感觉运动回路内信号的幅度，频率和相位的能力。但是，为了试验闭环控制，我们首先需要更好地了解系统的动力学以及自适应的工作原理。基于生物学的见识，我们开发了一种尖峰神经网络，能够连续不断地在线改变幅度，频率和相位。结果网络部署在六足机器人上，以观察步行行为。网络的形态和参数导致三脚架步态 证明没有传入反馈的设计足以维持稳定的步态。这可与生物学结果相媲美，生物学结果表明，脱去力的样品显示出类似三脚架的步态，并为网络拓扑在运动中发挥重要作用提供了证据。此外，这项工作使人们能够研究感觉反馈和高水平控制信号在步态类型适应中的作用。对运动神经控制的更好理解与生物学有关，它可以为目前在活昆虫上无法测试的理论提供证据。这项工作使人们能够研究感觉反馈和高水平控制信号在步态类型适应中的作用。对运动神经控制的更好理解与生物学有关，它可以为目前在活昆虫上无法测试的理论提供证据。这项工作使人们能够研究感觉反馈和高水平控制信号在步态类型适应中的作用。对运动神经控制的更好理解与生物学有关，它可以为目前在活昆虫上无法测试的理论提供证据。