Weakly supervised learning in neural encoding for the position of the moving finger of a macaque

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.