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首页> 外文期刊>Adaptive Behavior >Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks
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Classical conditioning in different temporal constraints: an STDP learning rule for robots controlled by spiking neural networks

机译：不同时间约束下的经典条件：由尖峰神经网络控制的机器人的STDP学习规则

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摘要

This work investigates adaptive behaviours for an intelligent robotic agent when subjected to temporal stimuli consisting of associations of contextual cues and simple reflexes. This is made possible thanks to a novel learning rule based on spike-timing-dependent plasticity and embedded in an artificial spiking neural network serving as a brain-like controller. The subsequent bio-inspired cognitive system carries out different classical conditioning tasks in a controlled virtual 3D-world while the timing and frequency of unconditioned and conditioned parameters are varied. The results of this simulated robotic environment are analysed at different stages from stimuli capture to neural spike generation and show extended behavioural capabilities by the robot in the temporal domain.
机译：这项工作调查了智能机器人特工在受到上下文线索和简单反射关联的时间刺激时的适应行为。这要归功于一种新颖的学习规则，该学习规则基于依赖于尖峰时序的可塑性，并嵌入在充当大脑样控制器的人工尖峰神经网络中。随后的受生物启发的认知系统在受控的虚拟3D世界中执行不同的经典调节任务，同时改变未调节和调节后参数的时间和频率。从刺激捕获到神经尖峰生成的不同阶段分析了此模拟机器人环境的结果，并显示了该机器人在时域中扩展的行为能力。

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