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Navigation via Pavlovian conditioning: A robotic bio-constrained model of autoshaping in rats

机译:通过巴甫洛夫条件导航:大鼠自体成型的机器人生物约束模型

摘要

Within the autonomous robotics literature, bio-inspired models of navigation in organisms (e.g. rats) usually rely on instrumental conditioning processes based on the learning of associations between places in the environment and navigation actions leading to rewarded goal places. This paper presents a neural-network model capable of solving navigation tasks on the basis of Pavlovian conditioning processes (`autoshapingu27) which allow transferring innate approaching behaviours from biologically salient stimuli (e.g., food) to neutral stimuli (e.g., a landmark seen from far away and close to the food). The overall architecture and functioning of the model is biologically constrained on the basis of relevant neuroscienti?c anatomical and physiological knowledge on amygdala, nucleus accumbens, and ventral tegmental area. The model is tested with a simulated robotic rat engaged in autoshaping and devaluation experiments. The results show that, although the model allows solving only simple navigation tasks, it produces fast learning and a ?exible sensitivity of behaviour to internal states typical of Pavlovian processes. The model is also important for the investigation of adaptive behaviour in general as it clari?es the nature of some Pavlovian core mechanisms which play a key role in several forms of learning.
机译:在自主机器人学文献中,生物启发的生物(例如大鼠)导航模型通常依赖于仪器调节过程,该过程基于对环境中位置与导致奖励目标位置的导航动作之间的关联的学习。本文提出了一个神经网络模型,该模型能够基于巴甫洛夫式调节过程(“ autoshaping u27”)解决导航任务,该过程允许将先天接近行为从生物显性刺激(例如食物)转移到中性刺激(例如可见的地标)从远处接近食物)。该模型的整体架构和功能在有关杏仁核,伏隔核和腹侧被盖区域的相关神经科学解剖和生理知识的基础上受到生物学限制。使用参与自动整形和贬值实验的模拟机器人老鼠对模型进行测试。结果表明,尽管该模型仅允许解决简单的导航任务,但它可产生快速学习并对行为表现出对巴甫洛夫过程典型内部状态的灵活敏感性。该模型对于适应性行为的总体研究也很重要,因为它阐明了一些巴甫洛夫式核心机制的性质,这些机制在几种学习形式中起着关键作用。

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