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Neural decoding plays a vital role in the interaction between the brain andthe outside world. In this paper, we directly decode the movement track of afinger based on the neural signals of a macaque. Supervised regression methodsmay overfit to actual labels containing noise, and require a high labelingcost, while unsupervised approaches often have unsatisfactory accuracy.Besides, the spatial and temporal information is often ignored or not wellexploited by those methods. This motivates us to propose a robustweakly-supervised method, called ViF-SD2E, for neural decoding. In particular,it consists of a space-division (SD) module and a exploration--exploitation(2E) strategy, to effectively exploit both the spatial information of theoutside world and the temporal information of neural activity, where the SD2Eoutput is analogized with the weak 0/1 vision-feedback (ViF) label fortraining. It is worth noting that the designed ViF-SD2E is based on a symmetricphenomenon between the unsupervised decoding trajectory and the real trajectoryin previous observations, then a cognitive pattern of fuzzy (robust)interaction in the nervous system may be discovered by us. Extensiveexperiments demonstrate the effectiveness of our method, which can be sometimescomparable to supervised counterparts.

ViF-SD2E: A Robust Weakly-Supervised Method for Neural Decoding