A Neuro-robot System Generating Collision Avoidance Behavior Using Short-term Depression Effects in a Cultured Neuronal Network
A Neuro-robot System Generating Collision Avoidance Behavior Using Short-term Depression Effects in a Cultured Neuronal Network
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利用培养神经元网络中的短期抑郁效应产生碰撞避免行为的神经机器人系统
DOI：
10.1109/scisisis55246.2022.10002119
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
N. Yamaguchi and S. N. Kudoh,
中科院分区：
文献类型：
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作者：
Ryota Namba;Jun Sakuma;Kai Hirokawa; Suguru N. Kudoh;N. Yamaguchi and S. N. Kudoh,
神经元网络中的突触可塑性已被证明对运动学习很重要。在这项研究中，我们分析了神经网络，通过机器人身体产生碰撞避免行为的这种神经机器人与外界相互作用，通过减少尖峰的自发电活动的数量，由于短期抑郁症（STD）诱导的重复电输入培养的神经网络。我们分析了STD效果的刺激模式，并进行了运行实验的神经机器人与最佳的刺激条件（10 Hz）。结果，机器人成功地执行了避碰行为。在重复的实验中，观察到增加的转向行为。实验结束后，自发电活动的峰频率有降低的趋势，峰放电的周期性间隔比实验前有增大的趋势。这些结果表明，避免障碍物的神经机器人的运行诱导长期抑郁症（LTD）也在培养的神经元网络，并增加自发神经电活动的放电周期的间隔。
Synaptic plasticity in a neuronal network has been shown to be important for motor learning. In this study, we analyzed the neuronal network that interacts with the outside world through the robot body generating collision avoidance behavior of this neuro-robot, by reducing the number of spikes in spontaneous electrical activity due to the short-term depression (STD) induced by repeated electrical inputs to the cultured neuronal network. We analyzed the STD effects by the stimulus patterns and performed a running experiment of the neuro-robot with the optimal stimulus condition (10 Hz). As a result, the robot successfully performed the collision avoidance behavior. During repeated experiments, increased turning behavior was observed. The spike frequency of spontaneous electrical activity tended to decrease after the running experiment, and the periodic intervals between spike firings tended to be larger than those before the experiment. These results suggest that the running of the neuro-robot with avoiding obstacles induces long-term depression (LTD) also in the cultured neuronal network and increases the interval of the firing cycle of spontaneous neuroelectric activity.