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

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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.