  Despite advances in artificial intelligence models, neural networks still
cannot achieve human performance, partly due to differences in how information
is encoded and processed compared to human brain. Information in an artificial
neural network (ANN) is represented using a statistical method and processed as
a fitting function, enabling handling of structural patterns in image, text,
and speech processing. However, substantial changes to the statistical
characteristics of the data, for example, reversing the background of an image,
dramatically reduce the performance. Here, we propose a quantum superposition
spiking neural network (QS-SNN) inspired by quantum mechanisms and phenomena in
the brain, which can handle reversal of image background color. The QS-SNN
incorporates quantum theory with brain-inspired spiking neural network models
from a computational perspective, resulting in more robust performance compared
with traditional ANN models, especially when processing noisy inputs. The
results presented here will inform future efforts to develop brain-inspired
artificial intelligence.
