本研究提出了一种基于Hebb原理和遗忘机制的新型人工神经网络模型，旨在模拟并接近生物大脑的学习与处理机制，即“类脑计算”。该模型深受生物神经科学的启发，尤其是大脑中的神经元连接和学习模式。与依赖传统反馈机制的神经网络不同，该模型强化了频繁协同激活的神经元间的连接，并结合了遗忘机制以降低不常用或不重要连接的权重。实验结果验证了模型设计的合理性和有效性，尤其是在特定分类任务上展现出了高准确率。本研究提出的模型有望在各类人工智能应用中发挥更加关键的作用。

This study presents a new artificial neural network model based on Hebbian principles and forgetting mechanisms, aimed at simulating and approximating the learning and processing mechanisms of the biological brain, or "brain-like computing". This model is deeply inspired by neuroscience, particularly the neural connections and learning patterns in the brain. Unlike traditional feedback-dependent neural networks, this model enhances the connections between neurons that are frequently co-activated and incorporates forgetting mechanisms to reduce the weights of infrequently used or less important connections. Experimental results validate the rationality and effectiveness of the model design, particularly demonstrating high accuracy in specific classification tasks. This model is expected to play a more crucial role in various artificial intelligence applications.