灵长类前额叶皮层在数量感知决策中的作用及其神经编码机制

数量感知是大脑对感觉信息的抽象加工，是人和动物进行行为决策的重要基础。我们先前工作发现，在猕猴的后顶叶皮层存在大量对数量有特征反应的神经元，且是相对数量分辨所必需的，但其环路连接机制尚不清楚。前额叶与顶叶有着广泛的相互投射，是更高级的信息整合皮层，和顶叶一道被认为是数认知的核心脑区。但其在数量感知决策中的确切角色尚缺乏核心证据，其编码机制也不明晰。为此，本项目拟训练猕猴进行基于视觉信号的相对数量判断任务操作，1）在后顶叶和前额叶多道同步记录神经电活动，从而定位前额叶的数量编码区域并观察分析后顶叶和前额叶皮层神经元的编码特征及协同关系；2）使用muscimol可逆失活前额叶相关区域，明确其重要性及是否有任务特异性；3）使用双光子钙成像观察前额叶III-V层神经元在任务操作时的集群反应，探讨从感知到决策过程的编码机制。本研究将为理解脑的运行机制和模拟脑的人工智能开发提供理论依据。

Numerosity is an abstract neural representation of sensory input, which is an important decision basis for both human and animals. In our previous studies, we identified the brain area in the posterior parietal cortex (PPC) where were neurons encoding numeric information and was functionally involved in the relative numerical discrimination. However, how the neural circuits are wired is largely unknown yet. The prefrontal cortex (PFC) is mutual connected with the parietal cortex, which is on the top layer to integrate neural information from sub-cortical areas. The PFC, together with parietal cortex, are suggested to be the critical brain areas for numerical cognition. So far, the exact role and neural encoding mechanism of PFC for the numeric information processing and numeric based decision is not well studied yet. In this study, we will train the rhesus monkeys to perform a numeric discrimination task based on visual stimuli. 1) we will record the neural activities from both parietal and prefrontal cortex simultaneously using multi-channel electrodes. This will help find out the most relevant area in PFC and figure out how these two areas cooperate to process numeric information. 2) we will use muscimol to transiently inactivate the identified PFC area to see whether it is necessary and specific for numerical discrimination. 3) we will perform two-photon calcium imaging in the identified PFC area to see how the neuron populations from each layer (III-V) are working through the perception to decision processing and analyze the potential mechanism. This study will help to build up a neural computation model to describe the neural circuits for abstract information extraction and using, which is useful for the development of artificial intelligence based on brain simulation.