A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.
A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment.
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基于分离神经网络控制闭环环境的生物智能与机械智能相结合的新型机器人系统
DOI：
10.1371/journal.pone.0165600
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Zheng X
中科院分区：
文献类型：
--
作者：
Li Y;Sun R;Wang Y;Li H;Zheng X
我们提出了一种新的机器人系统的架构融合生物和人工智能的神经控制器连接到外部代理的基础上。我们最初建立了一个框架，将分离的神经网络连接到移动的机器人系统，以实现一个现实的车辆。设计了一个由摄像机和两轮机器人组成的移动的机器人系统来完成目标搜索任务。我们修改了一个软件架构，并开发了一个自制的刺激发生器，通过简单的二项式编码/解码方案建立生物和人工组件之间的双向连接。在本文中，我们利用一个特定的分层分离神经网络的第一次作为神经控制器。基于我们的工作，神经文化被成功地用于控制人工代理，从而产生高性能。令人惊讶的是，在强直刺激训练下，由于神经网络的短期可塑性（一种强化学习），机器人的表现随着训练周期的增加而越来越好。与以往的工作相比，我们采用了一种有效的实验方案（即增加训练周期）来保证短期可塑性的发生，并初步证明了机器人性能的提高可以独立地由分离神经网络的可塑性发展引起。这种新的框架可以通过神经网络的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，也可以为基于分层神经网络内的双向信息交换的下一代神经假体的开发提供理论启示。
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.
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影响因子：
4
作者：
le Feber, J.;Rutten, W. L. C.;van Pelt, J.
通讯作者：
van Pelt, J.
DOI：
10.1073/pnas.0808113105
发表时间：
2008-12-09
影响因子：
11.1
作者：
Jarosiewicz, Beata;Chase, Steven M.;Schwartz, Andrew B.
通讯作者：
Schwartz, Andrew B.
影响因子：
6.1
作者：
Kim, Jinseok;Park, Jungyul;Park, Sukho
通讯作者：
Park, Sukho
影响因子：
25
作者：
Lamsa, K;Heeroma, JH;Kullmann, DM
通讯作者：
Kullmann, DM
DOI：
10.1073/pnas.0605643104
发表时间：
2007-01-02
影响因子：
11.1
作者：
Luczak, Artur;Bartho, Peter;Harris, Kenneth D.
通讯作者：
Harris, Kenneth D.