Pagan Marino, Tang Vincent D, Aoi Mikio C, Pillow Jonathan W, Mante Valerio, Sussillo David, Brody Carlos D
Princeton Neuroscience Institute, Princeton, NJ, USA.
Simons Initiative for the Developing Brain, Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK.
Nature. 2025 Mar;639(8054):421-429. doi: 10.1038/s41586-024-08433-6. Epub 2024 Nov 28.
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 domains, 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-making. 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.
根据情境信息灵活切换我们对外部刺激的反应能力，对于与复杂世界成功互动至关重要。情境依赖的计算在许多领域都是必要的，但其神经实现仍知之甚少。在这里，我们开发了一种新颖的大鼠行为任务，以研究情境依赖的决策证据选择和积累。在猴子和大鼠数据支持的假设下，我们首先从数学上表明，这种计算可以由三种动态解决方案支持，并且执行该任务的所有网络都实现了这些解决方案的组合。这些解决方案可以直接用实验数据进行识别和测试。我们进一步表明，现有的电生理和建模数据与这些解决方案的所有可能组合兼容，这表明不同个体可能使用不同的组合。为了研究个体受试者之间的变异性，我们开发了自动化的高通量方法来训练大鼠完成我们的任务，并使用这些方法训练了许多受试者。与理论预测一致，神经和行为分析揭示了大鼠之间存在显著的异质性，尽管任务表现总体良好。我们的理论进一步预测了行为和神经特征之间的特定联系，这在数据中得到了有力支持。总之，我们的结果提供了一个实验支持的理论框架，用于分析执行灵活决策任务的生物和人工系统中的个体变异性，为更高认知中个体变异性的细胞分辨率研究打开了大门，并更广泛地提供了对情境依赖计算神经机制的见解。
