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.
翻译:尽管人工智能模型有了进步,但神经网络仍然无法实现人类的性能,部分原因是与人类大脑相比,信息是如何编码和处理的不同。人工神经网络(ANN)中的信息使用统计方法,并作为一种恰当的功能处理,从而能够处理图像、文字和语音处理的结构模式。然而,数据统计特征的重大变化,例如,改变图像的背景,大大降低性能。在这里,我们提议了一个量子超常定位神经网络(QS-SNN),它受量子机制和大脑中现象的启发,可以处理图像背景颜色的逆转。QS-SNN从计算角度将量子理论与大脑启发的神经网络模型结合起来,从而导致与传统的ANN模型相比,特别是在处理噪音投入时,效果更加稳健。这里介绍的结果将指导未来开发大脑激励型人工智能的努力。