  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.
