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A Simple Aplysia-Like Spiking Neural Network to Generate Adaptive Behavior in Autonomous Robots

机译：一个简单的类似Aplysia的尖刺神经网络在自主机器人中产生自适应行为

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摘要

In this article, we describe an adaptive controller for an autonomous mobile robot with a simple structure. Sensorimotor connections were made using a three-layered spiking neural network (SNN) with only one hidden-layer neuron and synapses with spike timing-dependent plasticity (STDP). In the SNN controller, synapses from the hidden-layer neuron to the motor neurons received presynaptic modulation signals from sensory neurons, a mechanism similar to that of the withdrawal reflex circuit of the sea slug, Aplysia. The synaptic weights were modified dependent on the firing rates of the presynaptic modulation signal and that of the hidden-layer neuron by STDP. In experiments using a real robot, which uses a similar simple SNN controller, the robot adapted quickly to the given environment in a single trial by organizing the weights, acquired navigation and obstacle-avoidance behavior. In addition, it followed dynamical changes in the environment. This associative learning scheme can be a new strategy for constructing adaptive agents with minimal structures, and may be utilized as an essential mechanism of an SNN ensemble that binds multiple sensory inputs and generates multiple motor outputs.
机译：在本文中，我们描述了一种具有简单结构的自主移动机器人的自适应控制器。感觉运动连接是使用三层尖刺神经网络（SNN）进行的，其中只有一个隐层神经元和突触具有与时间相关的可塑性（STDP）。在SNN控制器中，从隐层神经元到运动神经元的突触接收到来自感觉神经元的突触前调制信号，该机制类似于海Ap Aplysia的退出反射回路的机制。根据STDP的突触前调制信号和隐层神经元的发射速率，调整突触权重。在使用真实机器人的实验中，该机器人使用类似的简单SNN控制器，该机器人通过组织权重，获得的导航和避障行为，在一次试验中迅速适应了给定的环境。此外，它跟随环境的动态变化。该关联学习方案可以是一种用于构建具有最小结构的自适应代理的新策略，并且可以用作绑定多个感官输入并生成多个运动输出的SNN集成的基本机制。

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  • 来源
    《Adaptive Behavior》 |2008年第5期|306-324|共19页
  • 作者

    Fady Alnajjar; Kazuyuki Murase;

  • 作者单位

    Department of System Design Engineering, University of Fukui, Japan;

    Department of System Design Engineering, University of Fukui, Japan Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, Japan Research and Education Program for Life Science, University of Fukui, Japan Department of Human and Artificial Intelligence Systems, Graduate School of Engineering, University of Fukui, 3-9-1 Bunkyo, Fukui 910-8507, Japan;

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  • 正文语种 eng
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  • 关键词

    aplysia; associative learning; autonomous mobile robot; presynaptic modulation; spike timing-dependent plasticity; spiking neural network;

    机译：失眠联想学习;自主移动机器人突触前调制峰值时间相关的可塑性尖刺神经网络;

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