Frontal cortex neuron types categorically encode single decision variables.
Frontal cortex neuron types categorically encode single decision variables.
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许多皮质区域的单个神经元已被发现编码与该区域功能相关的环境或身体的特定，可识别的特征。然而，在与认知有关的额叶皮层中，神经反应表现出令人困惑的复杂性，携带着看似混乱的感觉、运动和其他与任务相关的变量的混合物。这种复杂性导致了个体额叶神经元的表征是随机混合的，只能在神经群体水平上被理解。在这里，我们展示了大鼠眶额皮质（OFC）的神经活动是高度结构化的：在解释选择行为的计算模型中，单个神经元的活动与个体变量共同变化。为了在一个大的行为空间中描述神经反应，我们训练大鼠进行一项结合感知和价值导向决策的行为任务。一项无偏、无模型的聚类分析确定了不同的OFC神经元组，每个组在任务变量空间中都有特定的反应特征。将一个简单的选择行为模型应用于这些分类反应概况，揭示了每个概况定量地对应于一个特定的决策变量，如决策置信度。此外，我们证明了一种连接定义的细胞类型，即投射到纹状体的眶额神经元，选择性地编码单一决策变量（综合值）的时间持续表征。我们认为，额叶皮层的神经元与其他皮质区域一样，形成了与该区域功能相关的稀疏和过度完整的特征表征，并且它们有选择地将这些信息分发到下游区域以支持行为。
Individual neurons in many cortical regions have been found to encode specific, identifiable features of the environment or body that pertain to the function of the region. However in frontal cortex, which is involved in cognition, neural responses display baffling complexity, carrying seemingly disordered mixtures of sensory, motor, and other task-related variables. This complexity has led to the suggestion that representations in individual frontal neurons are randomly mixed and can only be understood at the neural population level. Here we show that neural activity in rat orbitofrontal cortex (OFC) is instead highly structured: single neuron activity co-varies with individual variables in computational models that explain choice behavior. To characterize neural responses across a large behavioral space, we trained rats on a behavioral task that combines perceptual and value-guided decisions. An unbiased, model-free clustering analysis identified distinct groups of OFC neurons, each with a particular response profile in task-variable space. Applying a simple model of choice behavior to these categorical response profiles revealed that each profile quantitatively corresponds to a specific decision variable, such as decision confidence. Additionally, we demonstrate that a connectivity-defined cell type, orbitofrontal neurons projecting to the striatum, selectively encodes temporally sustained representation of a single decision variable, integrated value. We propose that neurons in frontal cortex, as in other cortical regions, form a sparse and overcomplete representation of features relevant to the region’s function, and that they distribute this information selectively to downstream regions to support behavior.
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