The Synthetic Nervous System (SNS) is a biologically inspired neural network (NN). Due to its capability of capturing complex mechanisms underlying neural computation, an SNS model is a candidate for building compact and interpretable NN controllers for robots. Previous work on SNSs has focused on applying the model to the control of legged robots and the design of functional subnetworks (FSNs) to realize dynamical systems. However, the FSN approach has previously relied on the analytical solution of the governing equations, which is difficult for designing more complex NN controllers. Incorporating plasticity into SNSs and using learning algorithms to tune the parameters offers a promising solution for systematic design in this situation. In this paper, we theoretically analyze the computational advantages of SNSs compared with other classical artificial neural networks. We then use learning algorithms to develop compact subnetworks for implementing addition, subtraction, division, and multiplication. We also combine the learning-based methodology with a bioinspired architecture to design an interpretable SNS for the pick-and-place control of a simulated gantry system. Finally, we show that the SNS controller is successfully transferred to a real-world robotic platform without further tuning of the parameters, verifying the effectiveness of our approach.

翻译：合成神经系统（SNS）是一种受生物启发的神经网络（NN）。因其能够捕捉神经计算背后的复杂机制，SNS模型有望为机器人构建紧凑且可解释的神经网络控制器。此前关于SNS的研究主要聚焦于将该模型应用于腿式机器人控制，以及设计用于实现动力系统的功能子网络（FSN）。然而，FSN方法此前依赖控制方程的解析解，这为设计更复杂的神经网络控制器带来了困难。将可塑性融入SNS并利用学习算法调整参数，为此类场景下实现系统性设计提供了有前景的解决方案。本文首先从理论上分析了SNS相较于其他经典人工神经网络的计算优势，随后利用学习算法开发了用于实现加法、减法、除法和乘法的紧凑子网络。此外，我们将基于学习的方法与仿生架构相结合，设计了一个可解释的SNS，用于模拟龙门系统的抓取与放置控制。最后，我们证明该SNS控制器无需额外参数调整即可成功迁移至真实机器人平台，验证了所提方法的有效性。