Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of the Chinese Academy of Sciences, Beijing 100049, China.
Institute of Neuroscience, State Key Laboratory of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai 200031, China; University of the Chinese Academy of Sciences, Beijing 100049, China; Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai 201210, China.
Neuron. 2021 Jun 16;109(12):2009-2024.e6. doi: 10.1016/j.neuron.2021.04.014. Epub 2021 May 5.
Making flexible decisions based on prior knowledge about causal environmental structures is a hallmark of goal-directed cognition in mammalian brains. Although several association brain regions, including the orbitofrontal cortex (OFC), have been implicated, the precise neuronal circuit mechanisms underlying knowledge-based decision-making remain elusive. Here, we established an inference-based auditory categorization task where mice performed within-session flexible stimulus re-categorization by inferring the changing task rules. We constructed a reinforcement learning model to recapitulate the inference-based flexible behavior and quantify the hidden variables associated with task structural knowledge. Combining two-photon population imaging and projection-specific optogenetics, we found that auditory cortex (ACx) neurons encoded the hidden task rule variable, which requires feedback input from the OFC. Silencing OFC-ACx input specifically disrupted re-categorization behavior. Direct imaging from OFC axons in the ACx revealed task state-related feedback signals, supporting the knowledge-based updating mechanism. Our data reveal a cortical circuit mechanism underlying structural knowledge-based flexible decision-making.
基于先前对因果环境结构的了解做出灵活决策是哺乳动物大脑中目标导向认知的标志。尽管包括眶额皮质(OFC)在内的几个关联大脑区域都与决策相关,但支持基于知识的决策的精确神经元回路机制仍不清楚。在这里,我们建立了一个基于推理的听觉分类任务,其中老鼠通过推断不断变化的任务规则在单次会话中灵活地重新分类刺激。我们构建了一个强化学习模型来重现基于推理的灵活行为,并量化与任务结构知识相关的隐藏变量。结合双光子群体成像和投影特异性光遗传学,我们发现听觉皮层(ACx)神经元编码隐藏的任务规则变量,该变量需要来自眶额皮质(OFC)的反馈输入。特异性沉默 OFC-ACx 输入会破坏重新分类行为。来自 ACx 中 OFC 轴突的直接成像显示了与任务状态相关的反馈信号,支持基于知识的更新机制。我们的数据揭示了一个皮质回路机制,该机制支持基于结构知识的灵活决策。