Li Yongcheng, Sun Rong, Wang Yuechao, Li Hongyi, Zheng Xiongfei
State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, Liaoning, P. R. China.
University of Chinese Academy of Sciences, Beijing, P. R. China.
PLoS One. 2016 Nov 2;11(11):e0165600. doi: 10.1371/journal.pone.0165600. eCollection 2016.
We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot's performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.
我们提出了一种新型机器人系统的架构，该系统基于连接到外部智能体的神经控制器，融合了生物智能和人工智能。我们最初构建了一个框架，将分离的神经网络连接到移动机器人系统，以实现一个逼真的载具。以摄像头和两轮机器人为特征的移动机器人系统被设计用于执行目标搜索任务。我们修改了软件架构，并开发了一个自制的刺激发生器，通过简单的二项式编码/解码方案在生物组件和人工组件之间建立双向连接。在本文中，我们首次使用了一种特定的分层分离神经网络作为神经控制器。基于我们的工作，神经培养物成功地用于控制人工智能体，从而实现了高性能。令人惊讶的是，在破伤风刺激训练下，由于神经网络的短期可塑性（一种强化学习），机器人随着训练周期的增加表现越来越好。与之前报道的工作相比，我们采用了一种有效的实验方案（即增加训练周期）来确保短期可塑性的出现，并初步证明了分离神经网络的可塑性发展可以独立导致机器人性能的提高。这个新框架可能通过神经网络可塑性处理的工程应用为智能机器人的学习能力提供一些可能的解决方案，也为基于分层神经网络内信息双向交换的下一代神经假体的理论启发发展提供支持。
