Core Insights - The article highlights the integration of AI tools, particularly large models, into the core work of eight leading neuroscientists, showcasing their applications in various research scenarios such as cell mapping, literature analysis, data processing, experimental guidance, code generation, and meeting documentation [5][6]. Group 1: AI Applications in Neuroscience - AI has become an indispensable "collaborative partner" in scientific research, focusing on six main applications: generating unbiased brain cell maps (e.g., CellTransformer model), assisting non-native English researchers in improving text and coding efficiency, predicting neuroscience research outcomes and reproducibility (e.g., BrainGPT project), developing visualization tools for neural data exploration, guiding experiments related to disease mechanisms, and quickly extracting structured information from literature (e.g., MetaBeeAI) [5][6]. - AI primarily handles large-scale data processing, repetitive tasks, and preliminary analysis, while human researchers are responsible for result validation, error correction, in-depth interpretation, and key decision-making, ensuring accuracy and transparency in research through an "expert-in-the-loop" design [5][6]. Group 2: Breakthroughs and Limitations - AI can transcend human cognitive limitations by discovering uncataloged brain regions, connecting overlooked research findings, and rapidly processing vast amounts of literature and data, thus providing a scalable platform for cross-species and cross-disease research, accelerating scientific discovery [5][6]. - However, AI still faces challenges such as hallucinations (fabricated literature/data), statistical traps, and understanding biases. Researchers mitigate these risks by limiting the tools' operational boundaries, manually reviewing outputs, constructing domain benchmark datasets, and continuously fine-tuning prompts [5][6]. Group 3: Bidirectional Empowerment - A new trend of bidirectional empowerment is emerging, where AI not only aids neuroscience research but also leverages neuroscience findings to analyze AI models (e.g., exploring large model language processing mechanisms). This reciprocal exchange fosters a synergistic effect, promoting progress in both fields [6].