美国普林斯顿神经科学研究所Carlos D. Brody和Marino Pagan共同合作，近期取得重要工作进展。他们研究提出了灵活决策所依赖的神经计算的个体差异性。相关研究成果2024年11月28日在线发表于《自然》杂志上。

据介绍，根据环境信息灵活切换人们对外部刺激的反应的能力对于与复杂世界的成功互动至关重要。环境变化相关的计算在许多领域都是必要的，但它们的神经实现仍然知之甚少。

研究人员在大鼠身上开发了一项新的行为任务，研究情境依赖的选择和决策证据的积累。在猴子和老鼠数据支持的假设下，研究人员首先从数学上证明，这种计算可以得到三个动态解的支持，并且执行任务的所有网络都实现了这些解的组合。这些解决方案可以直接用实验数据进行识别和测试。进一步表明，现有的电生理和建模数据与这些解决方案的各种可能组合兼容，这表明不同的个体可以使用不同的组合。

为了研究个体受试者之间的变异性，研究人员开发了自动化、高通量的方法来训练大鼠完成我们的任务，研究人员还训练了许多受试者。与理论预测一致，神经和行为分析显示，尽管任务表现一致，但大鼠之间存在很大的异质性。这一理论进一步预测了行为和神经特征之间的特定联系，这在数据中得到了强有力的支持。

总之，这一研究结果为分析执行灵活决策任务的生物和人工系统中的个体差异提供了一个新的实验支持的理论框架，为更高认知中个体差异的细胞分辨率研究打开了大门，并更广泛地提供了对环境相关计算的神经机制的见解。

附：英文原文

Title: Individual variability of neural computations underlying flexible decisions

Author: Pagan, Marino, Tang, Vincent D., Aoi, Mikio C., Pillow, Jonathan W., Mante, Valerio, Sussillo, David, Brody, Carlos D.

Issue&Volume: 2024-11-28

Abstract: The ability to flexibly switch our response to external stimuli according to contextual information is critical for successful interactions with a complex world. Context-dependent computations are necessary across many domains1–3, yet their neural implementations remain poorly understood. Here we developed a novel behavioral task in rats to study context-dependent selection and accumulation of evidence for decision-making46. Under assumptions supported by both monkey and rat data, we first show mathematically that this computation can be supported by three dynamical solutions, and 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 modeling 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 we trained many subjects on it. Consistent with theoretical predictions, neural and behavioral analyses revealed substantial heterogeneity across rats, despite uniformly good task performance. Our theory further predicts a specific link between behavioral and neural signatures, which was robustly supported in the data. In summary, our results provide a new experimentally-supported theoretical framework to analyze individual variability in biological and artificial systems performing flexible decision-making tasks, they open the door to cellular-resolution studies of individual variability in higher cognition, and they provide insights into neural mechanisms of context-dependent computation more generally.

DOI: 10.1038/s41586-024-08433-6

Source: https://www.nature.com/articles/s41586-024-08433-6