摘要：SummaryDespite advances in artificial intelligence models, neural networks still cannot achieve human performance, partly due to differences in how information is encoded and processed compared with 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.Graphical abstractDisplay OmittedHighlights•Quantum and neuroscience research inspire new methods for artificial intelligence•QS-SNN integrates characteristics of quantum superposition state and brain spikes•QS-SNN can handle the background reverse visual inputs well•QS-SNN model is robust to reverse pixels noise and Gaussian noise on visual inputsQuantum theory; Artificial intelligence; Artificial intelligence theory
