Hopfield增强的深度神经网络用于抗伪影的脑状态解码
Hopfield-Enhanced Deep Neural Networks for Artifact-Resilient Brain State Decoding
AI总结：
本研究提出一种结合Hopfield网络预处理与CNN分类的两阶段框架，用于抗伪影的脑状态解码，实验表明该框架在噪声条件下能达到与干净数据模型相当的性能。
摘要：
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），对于评估大脑的时空动态及其与行为的密切关联至关重要。然而，开发准确识别这些状态的新技术仍然是一个挑战，因为这些技术常常受到噪声、伪影和次优记录质量的影响。在本研究中，我们提出了一种两阶段计算框架，将Hopfield网络用于伪影数据预处理，并结合卷积神经网络（CNNs）在不同麻醉水平下对大鼠神经记录中的脑状态进行分类。为了评估我们框架的鲁棒性，我们有意在神经记录中引入噪声伪影。我们通过将混合Hopfield-CNN流水线与两个对比模型进行基准测试来评估其性能：一个处理相同噪声输入的独立CNN，另一个在无伪影数据上训练和测试的CNN。在不同数据压缩水平和噪声强度下的性能表明，我们的框架能有效减轻伪影，使模型在较低噪声水平下达到与干净数据CNN相当的性能。尽管这项研究主要惠及小规模实验，但研究结果强调了先进深度学习和Hopfield网络模型在多样化现实场景中提高可扩展性和鲁棒性的必要性。
英文摘要：
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.
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