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——涵盖从高度同步到异步的神经元模式（如睡眠-觉醒周期）——对于评估大脑的时空动态及其与行为的紧密联系至关重要。然而，开发准确识别这些状态的新技术仍面临挑战，因为噪声、伪迹和次优记录质量常使其性能受损。本研究提出一个两阶段计算框架，结合Hopfield网络进行伪迹数据预处理，并使用卷积神经网络（CNN）对不同麻醉水平下大鼠神经记录中的脑状态进行分类。为评估框架的鲁棒性，我们人为地将噪声伪迹引入神经记录。通过将混合Hopfield-CNN流水线与两个对比模型——一个处理相同噪声输入的独立CNN，以及另一个在无伪迹数据上训练和测试的CNN——进行基准测试，评估其性能。在不同数据压缩度和噪声强度下的结果表明，我们的框架能有效缓解伪迹，使模型在较低噪声水平下达到与清洁数据CNN相当的性能。尽管本研究主要受益于小规模实验，但研究结果强调了改进深度学习与Hopfield网络模型以提升其在多样化现实场景中可扩展性和鲁棒性的必要性。