The problem of neural decoding is essential for the realization of a neural interface. In this study, the position of the moving finger of a macaque was directly decoded through the neuron spike signals in the motor cortex, instead of relying on the synergy of the related muscle tissues around the body, also known as neural decoding. Currently, supervised learning is the most commonly employed method for this purpose. However, based on existing technologies, unsupervised learning with regression causes excessive errors. To solve this problem, weakly supervised learning (WSL) was used to correct the predicted position of the moving finger of a macaque in unsupervised training. Then, the corrected finger position was further used to train and accurately fit the weight parameters. We then utilized public data to evaluate the decoding performance of the Kalman filter (KF) and the expectation maximization (EM) algorithms in the WSL model. Unlike in previous methods, in WSL, the only available information is that the finger has moved to four areas in the plane, instead of the actual track value. When compared to the supervised models, the WSL decoding performance only differs by approximately 0.4%. This result improves by 41.3% relative to unsupervised models in the two-dimensional plane. The investigated approach overcomes the instability and inaccuracy of unsupervised learning. What’s more, the method in the paper also verified that the unsupervised encoding and decoding technology of neuronal signals is related to the range of external activities, rather than having a priori specific location.

中文翻译：

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猕猴移动手指位置的神经编码中的弱监督学习

神经解码的问题对于神经接口的实现至关重要。在这项研究中，通过运动皮层中的神经元尖峰信号直接解码了猕猴的活动手指的位置，而不是依靠身体周围相关肌肉组织的协同作用，这也称为神经解码。当前，监督学习是为此目的最常用的方法。但是，基于现有技术，带有回归的无监督学习会导致过多的错误。为了解决此问题，在无监督训练中使用了弱监督学习（WSL）来校正猕猴活动手指的预测位置。然后，将校正后的手指位置进一步用于训练和准确拟合体重参数。然后，我们利用公共数据来评估WSL模型中的卡尔曼滤波器（KF）和期望最大化（EM）算法的解码性能。与以前的方法不同，在WSL中，唯一可用的信息是手指已移至平面中的四个区域，而不是实际的轨迹值。与监督模型相比，WSL解码性能仅相差约0.4％。相对于二维平面中的无监督模型，此结果提高了41.3％。研究方法克服了无监督学习的不稳定性和不准确性。此外，本文方法还验证了神经元信号的无监督编码和解码技术与外部活动的范围有关，而不是具有先验的特定位置。