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ACC 神经集合动力学由策略优势度结构形成。

ACC neural ensemble dynamics are structured by strategy prevalence.

机构信息

Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, United States.

Department of Neuroscience, Johns Hopkins University Medical School, Baltimore, United States.

出版信息

Elife. 2023 Nov 22;12:e84897. doi: 10.7554/eLife.84897.

Abstract

Medial frontal cortical areas are thought to play a critical role in the brain's ability to flexibly deploy strategies that are effective in complex settings, yet the underlying circuit computations remain unclear. Here, by examining neural ensemble activity in male rats that sample different strategies in a self-guided search for latent task structure, we observe robust tracking during strategy execution of a summary statistic for that strategy in recent behavioral history by the anterior cingulate cortex (ACC), especially by an area homologous to primate area 32D. Using the simplest summary statistic - strategy prevalence in the last 20 choices - we find that its encoding in the ACC during strategy execution is wide-scale, independent of reward delivery, and persists through a substantial ensemble reorganization that accompanies changes in global context. We further demonstrate that the tracking of reward by the ACC ensemble is also strategy-specific, but that prevalence is insufficient to explain the observed activity modulation during strategy execution. Our findings argue that ACC ensemble dynamics is structured by a summary statistic of recent behavioral choices, raising the possibility that ACC plays a role in estimating - through statistical learning - which actions promote the occurrence of events in the environment.

摘要

额皮质区域被认为在大脑灵活运用策略的能力中起着关键作用,这些策略在复杂环境中是有效的,但潜在的回路计算仍不清楚。在这里,通过检查雄性大鼠在自我引导的寻找潜在任务结构的过程中采样不同策略时的神经集合活动,我们观察到在策略执行过程中,前扣带皮层(ACC)会对该策略的最近行为历史中的一个汇总统计数据进行强有力的跟踪,尤其是在与灵长类动物 32D 区域同源的区域。使用最简单的汇总统计信息 - 最近 20 次选择中的策略出现频率 - 我们发现,在策略执行过程中,其在 ACC 中的编码是广泛的,与奖励传递无关,并通过伴随全局上下文变化而发生的大量集合体重组而持续存在。我们进一步证明,ACC 集合体对奖励的跟踪也是策略特异性的,但出现频率不足以解释在策略执行过程中观察到的活动调制。我们的研究结果表明,ACC 集合体的动态是由最近行为选择的汇总统计信息构成的,这提出了一种可能性,即 ACC 通过统计学习来估计哪些行为会促进环境中事件的发生。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9f04/10703442/1bd35d672612/elife-84897-fig1.jpg

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