Computational Neuroscience is a rapidly evolving field that seeks to understand the brain’s complex functions through computational models and simulations. Recently, the integration of Artificial Intelligence (AI) and Machine Learning (ML) has revolutionized traditional signal processing methods, offering new avenues for decoding, modeling, and predicting neural activities. Despite significant advancements, several gaps remain, particularly in the seamless integration of ML techniques with existing neuroscience frameworks. Current studies have shown promising results in areas such as brain-computer interfaces and neural network modeling, yet the full potential of these technologies is far from being realized. Addressing these gaps requires a concerted effort to refine ML algorithms, validate computational models against empirical data, and explore novel applications in neurobiological research.This research topic aims to explore the deployment of machine learning for signal processing