The study of brain states, ranging from highly synchronous to asynchronous neuronal patterns like the sleep-wake cycle, is fundamental for assessing the brain's spatiotemporal dynamics and their close connection to behavior. However, the development of new techniques to accurately identify them still remains a challenge, as these are often compromised by the presence of noise, artifacts, and suboptimal recording quality. In this study, we propose a two-stage computational framework combining Hopfield Networks for artifact data preprocessing with Convolutional Neural Networks (CNNs) for classification of brain states in rat neural recordings under different levels of anesthesia. To evaluate the robustness of our framework, we deliberately introduced noise artifacts into the neural recordings. We evaluated our hybrid Hopfield-CNN pipeline by benchmarking it against two comparative models: a standalone CNN handling the same noisy inputs, and another CNN trained and tested on artifact-free data. Performance across various levels of data compression and noise intensities showed that our framework can effectively mitigate artifacts, allowing the model to reach parity with the clean-data CNN at lower noise levels. Although this study mainly benefits small-scale experiments, the findings highlight the necessity for advanced deep learning and Hopfield Network models to improve scalability and robustness in diverse real-world settings.

翻译：脑状态的研究，从高度同步到异步的神经元模式（如睡眠-觉醒周期），是评估大脑时空动态及其与行为密切关系的基础。然而，开发精确识别这些脑状态的新技术仍是一项挑战，因为这些状态常受到噪声、伪影及次优记录质量的影响。本研究提出一个两阶段计算框架，结合霍普菲尔德网络进行伪影数据预处理，并利用卷积神经网络分类不同麻醉深度下大鼠神经记录的脑状态。为评估框架的鲁棒性，我们有意在神经记录中引入噪声伪影。通过将混合霍普菲尔德-卷积神经网络流水线与两个对比模型进行基准测试：一个处理相同噪声输入的独立卷积神经网络，另一个在无伪影数据上训练和测试的卷积神经网络。在不同数据压缩比和噪声强度下的性能评估表明，我们的框架能有效减轻伪影影响，使模型在较低噪声水平下达到与无伪影数据训练的卷积神经网络相当的性能。尽管本研究主要惠及小规模实验，但结果凸显了开发先进深度学习与霍普菲尔德网络模型以提升在多样化真实场景中可扩展性和鲁棒性的必要性。