Quantum superposition inspired spiking neural network

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