Artificial intelligence and deep learning have revolutionized the field of neural data analysis in recent years. The explosion of complex, high-dimensional neural datasets from diverse modalities—such as electrophysiology, calcium imaging, and non-invasive brain imaging—has exposed significant limitations in traditional analytical methodologies. These challenges include difficulties in capturing subtle spatiotemporal patterns, extracting interpretable features, and achieving high predictive accuracy in brain decoding and functional mapping. Currently, researchers are leveraging powerful AI approaches, especially deep learning, to tackle these issues by automatically identifying intricate data patterns and relationships. Breakthroughs using convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and graph neural networks (GNNs) have already demonstrated substantial improvements in tasks such as neural signal classification, spike sorting, and network connectivity analysis. However, the field continues to grapple with critical gaps, most notably in the interpretability and transparency of models, scaling these methods for massive neural datasets, and ensuring that AI-derived insights translate into actionable knowledge in neuroscience and neural engineering applications.

This Research Topic aims to spotlight the most promising and innovative applications of artificial intelligence and deep learning, particularly trending approaches, for neural data analysis. We seek contributions that introduce or rigorously evaluate advanced methodologies—such as CNNs, RNNs, transformers, GNNs, reinforcement learning, self-supervised and unsupervised models, and generative frameworks—to enhance our understanding of neural systems. The objective is to catalyze progress by addressing existing technical and conceptual barriers and bridging the gap between methodological research and its practical impact on neuroscience, brain health, and neural technology.

The scope of this Research Topic encompasses both foundational and applied research in AI-driven neural data analysis, focusing on current and emerging machine learning techniques. We particularly encourage work that pushes the boundaries of scalability, interpretability, and translational relevance. To gather further insights, we welcome articles addressing, but not limited to, the following themes:

o Development and application of novel deep learning architectures (e.g., CNNs, RNNs, transformers, GNNs) for neural data interpretation

o Comparative evaluations of trending AI approaches versus traditional analytic practices

o Improvements in model explainability, interpretability, and transparency in neural contexts

o Scalable machine learning and deep learning pipelines for large-scale or high-throughput neural data

o Integration of reinforcement learning, self-supervised, and generative models in neural data analysis

o Case studies demonstrating successful AI-driven neuroscience or neural engineering applications

o Tools and frameworks that accelerate translation of AI models into clinical or industrial settings

We encourage submissions in the form of original research articles, reviews, and case studies.

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Artificial intelligence and deep learning for neural data analysis