英国牛津大学Timothy E. J. Behrens和Mohamady El-Gaby共同合作，近期取得重要工作进展。他们研究提出了绘制行为结构的细胞基础。相关研究成果2024年11月6日在线发表于《自然》杂志上。

据介绍，为了灵活地适应新的情况，大脑必须了解世界的规律，以及人类自己的行为模式。大量的发现开始揭示人类用来绘制外部世界地图的算法。然而，映射人类为实现目标而组成的复杂结构化行为的生物算法仍然未知。

研究人员揭示了一种用于映射抽象行为结构并将其转移到新场景的算法的神经元实现。研究人员训练小鼠完成许多任务，这些任务具有共同的结构（组织一系列目标），但在具体的目标位置上有所不同。小鼠发现了潜在的任务结构，使零样本推论在新任务的第一次试验中成为可能。内侧额叶皮层中大多数神经元的活动都会向目标推进，类似于位置细胞映射物理空间的方式。这些“目标进度单元”被泛化，拉伸和压缩它们的平铺，以适应不同的目标距离。

相比之下，整个目标序列的进展没有明确编码。相反，“目标进展细胞”的一个子集被进一步调整，使得单个神经元在特定行为步骤的固定任务滞后下被激活。这些细胞共同充当任务结构的记忆缓冲区，实现了一种算法，该算法对未来行为步骤的整个序列进行即时编码，其动态自动计算出每一步的适当动作。这些动态反映了任务中和离线睡眠期间的抽象任务结构。

总之，这一研究结果表明，复杂行为结构的图式可以通过将进度调整到单个行为步骤的任务结构缓冲区中来生成。

附：英文原文

Title: A cellular basis for mapping behavioural structure

Author: El-Gaby, Mohamady, Harris, Adam Loyd, Whittington, James C. R., Dorrell, William, Bhomick, Arya, Walton, Mark E., Akam, Thomas, Behrens, Timothy E. J.

Issue&Volume: 2024-11-06

Abstract: To flexibly adapt to new situations, our brains must understand the regularities in the world, as well as those in our own patterns of behaviour. A wealth of findings is beginning to reveal the algorithms that we use to map the outside world1,2,3,4,5,6. However, the biological algorithms that map the complex structured behaviours that we compose to reach our goals remain unknown. Here we reveal a neuronal implementation of an algorithm for mapping abstract behavioural structure and transferring it to new scenarios. We trained mice on many tasks that shared a common structure (organizing a sequence of goals) but differed in the specific goal locations. The mice discovered the underlying task structure, enabling zero-shot inferences on the first trial of new tasks. The activity of most neurons in the medial frontal cortex tiled progress to goal, akin to how place cells map physical space. These ‘goal-progress cells’ generalized, stretching and compressing their tiling to accommodate different goal distances. By contrast, progress along the overall sequence of goals was not encoded explicitly. Instead, a subset of goal-progress cells was further tuned such that individual neurons fired with a fixed task lag from a particular behavioural step. Together, these cells acted as task-structured memory buffers, implementing an algorithm that instantaneously encoded the entire sequence of future behavioural steps, and whose dynamics automatically computed the appropriate action at each step. These dynamics mirrored the abstract task structure both on-task and during offline sleep. Our findings suggest that schemata of complex behavioural structures can be generated by sculpting progress-to-goal tuning into task-structured buffers of individual behavioural steps.

DOI: 10.1038/s41586-024-08145-x

Source: https://www.nature.com/articles/s41586-024-08145-x