Computational mechanisms of the head direction circuit development shaped by visuomotor experience

由视觉运动经验塑造的头部方向回路发展的计算机制

• 批准号: 21K20679
• 负责人: Zeng Taiping
• 金额: $ 1.91万
• 依托单位: The University of Tokyo
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Research Activity Start-up
• 财政年份: 2021
• 资助国家: 日本
• 起止时间: 2021-08-30 至 2023-03-31
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-21K20679/
• 关键词: Head direction cells; Unsupervised learning; Self-organization; Spatial navigation; Robotics; Mobile robots; Development

Until now, I proposed a framework for unsupervised learning of head direction representations, which is implemented as a robotic system in customized virtual physical environments.First, I set up a robotic platform for data recording and learning algorithm training and testing.Second, I developed an unsupervised deep neural network, which can automatically form head direction representations during the training process.Third, I tested the performance of unsupervised head direction neural networks. The error between the predicted head direction and ground truth is between -21 degrees and 27 degrees with a root mean square error of about 11.1 degrees. The tuning curves of head direction cells binned by 4 degrees show a periodic bump to represent the head direction.

到目前为止，我提出了一个无监督学习的框架，该框架在定制的虚拟物理环境中作为机器人系统实现。网络。预测的头方向和地面真理之间的误差在-21度和27度之间，均方根误差约为11.1度。由4度组合的头部方向细胞的调谐曲线显示出定期凸起，以表示头部方向。