Individual variability of neural computations underlying flexible decisions

• 成果类型: Article
• 署名作者: Pagan, Marino; Tang, Vincent D.; Aoi, Mikio C.; Pillow, Jonathan W.; Mante, Valerio; Sussillo, David; Brody, Carlos D.
• 署名单位: University of Edinburgh; University of California System; University of California San Diego; University of Zurich; Swiss Federal Institutes of Technology Domain; ETH Zurich; Stanford University; Stanford University; Howard Hughes Medical Institute
• 刊物名称: Nature
• ISSN/ISSBN: 0028-2214
• DOI: 10.1038/s41586-024-08433-6
• 发表日期: 2025-03-13
• 关键词: prefrontal cortex; DYNAMICS; network; attention; selection; task

摘要

The ability to flexibly switch our responses to external stimuli according to contextual information is critical for successful interactions with a complex world. Context-dependent computations are necessary across many domains1, 2-3, yet their neural implementations remain poorly understood. Here we developed a novel behavioural task in rats to study context-dependent selection and accumulation of evidence for decision-making4, 5-6. Under assumptions supported by both monkey and rat data, we first show mathematically that this computation can be supported by three dynamical solutions and that all networks performing the task implement a combination of these solutions. These solutions can be identified and tested directly with experimental data. We further show that existing electrophysiological and modelling data are compatible with the full variety of possible combinations of these solutions, suggesting that different individuals could use different combinations. To study variability across individual subjects, we developed automated, high-throughput methods to train rats on our task and trained many subjects using these methods. Consistent with theoretical predictions, neural and behavioural analyses revealed substantial heterogeneity across rats, despite uniformly good task performance. Our theory further predicts a specific link between behavioural and neural signatures, which was robustly supported in the data. In summary, our results provide an experimentally supported theoretical framework to analyse individual variability in biological and artificial systems that perform flexible decision-making tasks, open the door to cellular-resolution studies of individual variability in higher cognition, and provide insights into neural mechanisms of context-dependent computation more generally.