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Positioning spontaneous activity as ‘Adhesive Dots’: Lessons from AI for data integration in neuroscience
In the previous article, I argued that advancing data integration in neuroscience requires incorporating resting-state spontaneous activity into each experiment, framing it as ‘adhesive dots.’ Here, I extend that discussion by drawing strategic lessons from the success of large language models (LLMs) and by concretizing the earlier claims from the perspective of data.
A ten-minute brain rest lets AI connect the dots across neuroscience
Dr. Masanori Shimono from Keio University highlights the importance of recording spontaneous neural activity in neuroscience and suggests that modern AI techniques can reveal meaningful patterns, offering insights into fundamental brain functions.
Keio University School of Medicine
Keio University School of Medicine is focusing its efforts on translational research, a far-sighted approach that embodies founder Yukichi Fukuzawa's scientific philosophy of combining the basic medical sciences and clinical science.
