A collaborative effort between the team at Ming[omitted] Technology and the Institute of Automation, Chinese Academy of Sciences, has yielded VisionHOPE—the inaugural general-purpose visual backbone network that incorporates a self-modifying learning system at its core. This innovative network possesses the capability to dynamically adjust its learning rules in response to the image content it has already processed during the forward computation of a single image. Employing sophisticated design elements such as five distinct types of memory coordination, dual-layer stability constraints, and four-directional block scanning, VisionHOPE strikes an optimal balance between performance and computational efficiency. It outperforms mainstream architectures of comparable scale, including CNN, ViT, and Mamba, across multiple visual task benchmarks, notably ImageNet-1K. The related research achievements have garnered significant attention, ranking highly on Hugging Face's daily paper list. Furthermore, all code, underlying operators, and pre-trained weights have been made openly available under the MIT license, facilitating development and invocation at various levels and paving the way for future research endeavors.