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

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，该框架作为机器人系统在定制的虚拟物理环境中实现。首先，我搭建了一个机器人平台，用于数据记录和学习算法的训练和测试。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，我测试了无监督的头部方向神经网络的性能。预测的头部方向与实际情况之间的误差在-21度到27度之间，均方根误差约为11.1度。头部方向细胞被分割4度的调谐曲线显示一个周期性的凸起来表示头部方向。

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.