  Neural decoding plays a vital role in the interaction between the brain and
the outside world. In this paper, we directly decode the movement track of a
finger based on the neural signals of a macaque. Supervised regression methods
may overfit to actual labels containing noise, and require a high labeling
cost, while unsupervised approaches often have unsatisfactory accuracy.
Besides, the spatial and temporal information is often ignored or not well
exploited by those methods. This motivates us to propose a robust
weakly-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 the
outside world and the temporal information of neural activity, where the SD2E
output is analogized with the weak 0/1 vision-feedback (ViF) label for
training. It is worth noting that the designed ViF-SD2E is based on a symmetric
phenomenon between the unsupervised decoding trajectory and the real trajectory
in previous observations, then a cognitive pattern of fuzzy (robust)
interaction in the nervous system may be discovered by us. Extensive
experiments demonstrate the effectiveness of our method, which can be sometimes
comparable to supervised counterparts.
