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首页> 外文期刊>Frontiers in Neurorobotics >Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm
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Cortical Spiking Network Interfaced with Virtual Musculoskeletal Arm and Robotic Arm

机译：与虚拟肌肉骨骼臂和机器人臂接口的皮质钉刺网络

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Embedding computational models in the physical world is a critical step towards constraining their behavior and building practical applications. Here we aim to drive a realistic musculoskeletal arm model using a biomimetic cortical spiking model, and make a robot arm reproduce the same trajectories in real time. Our cortical model consisted of a 3-layered cortex, composed of several hundred spiking model-neurons, which display physiologically realistic dynamics. We interconnected the cortical model to a two-joint musculoskeletal model of a human arm, with realistic anatomical and biomechanical properties. The virtual arm received muscle excitations from the neuronal model, and fed back proprioceptive information, forming a closed-loop system. The cortical model was trained using spike timing-dependent reinforcement learning to drive the virtual arm in a 2D reaching task. Limb position was used to simultaneously control a robot arm using an improved network interface. Virtual arm muscle activations responded to motoneuron firing rates, with virtual arm muscles lengths encoded via population coding in the proprioceptive population. After training, the virtual arm performed reaching movements which were smoother and more realistic than those obtained using a simplistic arm model. This system provided access to both spiking network properties and to arm biophysical properties, including muscle forces. The use of a musculoskeletal virtual arm and the improved control system allowed the robot arm to perform movements which were smoother than those reported in our previous paper using a simplistic arm. This work provides a novel approach consisting of bidirectionally connecting a cortical model to a realistic virtual arm, and using the system output to drive a robotic arm in real time. Our techniques are applicable to the future development of brain neuroprosthetic control systems, and may enable enhanced brain-machine interfaces with the possibility for finer control of limb prosthetics.
机译：将计算模型嵌入物理世界是限制其行为并建立实际应用的关键一步。在这里，我们的目标是使用仿生皮质尖峰模型来驱动逼真的肌肉骨骼模型，并使机器人手臂实时重现相同的轨迹。我们的皮层模型由三层皮层组成，该皮层由数百个尖峰模型神经元组成，可显示生理上逼真的动态。我们将皮质模型与人手臂的两关节肌肉骨骼模型互连，具有现实的解剖学和生物力学特性。虚拟手臂从神经元模型接收肌肉刺激，并反馈本体感受信息，形成一个闭环系统。皮质模型使用与峰值时间相关的强化学习来训练，以在2D到达任务中驱动虚拟手臂。肢体位置用于通过改进的网络接口同时控制机器人手臂。虚拟手臂肌肉的激活对运动神经元的放电速度有反应，虚拟手臂的长度是通过本体感受群体中的群体编码来编码的。训练后，虚拟手臂执行的到达动作比使用简单手臂模型获得的动作更平滑，更真实。该系统提供了对尖峰网络特性和手臂生物物理特性（包括肌肉力）的访问。肌肉骨骼虚拟手臂的使用和改进的控制系统使机器人手臂能够比我们以前使用简单手臂的论文中报道的动作更平滑。这项工作提供了一种新颖的方法，包括将皮质模型双向连接到真实的虚拟手臂，并使用系统输出实时驱动机器人手臂。我们的技术适用于脑神经假体控制系统的未来发展，并且可以增强脑机界面，并有可能更好地控制肢体假体。

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