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，我提出了一个头部方向表示的无监督学习框架，在定制的虚拟物理环境中实现为机器人系统。首先，我建立了一个机器人平台，用于数据记录和学习算法训练和测试。其次，我开发了一个无监督的深度神经网络，它可以在训练过程中自动形成头部方向表示。第三，我测试了无监督头部方向神经网络的性能。预测的头部方向和地面实况之间的误差在-21度和27度之间，均方根误差约为11.1度。以4度分仓的头部方向单元的调谐曲线示出了表示头部方向的周期性凸起。