Abstract representations emerge in human hippocampal neurons during inference

• 成果类型: Article
• 署名作者: Courellis, Hristos S.; Minxha, Juri; Cardenas, Araceli R.; Kimmel, Daniel L.; Reed, Chrystal M.; Valiante, Taufik A.; Salzman, C. Daniel; Mamelak, Adam N.; Fusi, Stefano; Rutishauser, Ueli
• 署名单位: Cedars Sinai Medical Center; California Institute of Technology; Columbia University; Krembil Research Institute; University of Toronto; University Health Network Toronto; University of Toronto; University Health Network Toronto; Columbia University; Cedars Sinai Medical Center; New York State Psychiatry Institute; Columbia University; Columbia University; Cedars Sinai Medical Center
• 刊物名称: Nature
• ISSN/ISSBN: 0028-6009
• DOI: 10.1038/s41586-024-07799-x
• 发表日期: 2024-08-22
• 关键词: medial temporal-lobe; cognitive map; models; cortex; connectionist; INFORMATION; KNOWLEDGE; geometry; complex; systems

摘要

Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization1. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour2,3. Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition. A task in which participants learned to perform inference led to the formation of hippocampal representations whose geometric properties reflected the latent structure of the task, indicating that abstract or disentangled neural representations are important for complex cognition.