论文标题
从生物突触到智能机器人
From Biological Synapses to Intelligent Robots
论文作者
论文摘要
这篇评论探讨了受生物学启发的学习，作为智能机器人控制和传感技术的模型，基于特定示例。如从无脊椎动物和脊椎动物的高度塑料生物学神经网络中解释的基础上，将Hebbian突触学习作为机器学习和智力的功能相关模型进行了讨论。它在没有监督的情况下进行自适应学习和控制的潜力，功能复杂性的产生以及基于自我组织的控制体系结构的潜力得到了发展。基于兴奋性和抑制性神经机制而没有先验知识的学习解释了生存或任务相关表示的过程。无监督的生物学习的基本机制驱动了突触可塑性和适应具有不同复杂程度的活体大脑行为成功的基本机制。在这里收集的见解将Hebbian模型作为智能机器人和传感器系统的选择解决方案。关键词：Hebbian学习，突触可塑性，神经网络，自我组织，大脑，增强，感觉处理，机器人控制
This review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the highly plastic biological neural networks of invertebrates and vertebrates. Its potential for adaptive learning and control without supervision, the generation of functional complexity, and control architectures based on self organization is brought forward. Learning without prior knowledge based on excitatory and inhibitory neural mechanisms accounts for the process through which survival or task relevant representations are either reinforced or suppressed. The basic mechanisms of unsupervised biological learning drive synaptic plasticity and adaptation for behavioral success in living brains with different levels of complexity. The insights collected here point toward the Hebbian model as a choice solution for intelligent robotics and sensor systems. Keywords: Hebbian learning, synaptic plasticity, neural networks, self organization, brain, reinforcement, sensory processing, robot control
