Animal Physiology, Institute of Neurobiology, University of Tübingen, 72076 Tübingen, Germany.
Proc Natl Acad Sci U S A. 2010 Feb 2;107(5):2277-82. doi: 10.1073/pnas.0909180107. Epub 2010 Jan 19.
Mathematics is based on highly abstract principles, or rules, of how to structure, process, and evaluate numerical information. If and how mathematical rules can be represented by single neurons, however, has remained elusive. We therefore recorded the activity of individual prefrontal cortex (PFC) neurons in rhesus monkeys required to switch flexibly between "greater than" and "less than" rules. The monkeys performed this task with different numerical quantities and generalized to set sizes that had not been presented previously, indicating that they had learned an abstract mathematical principle. The most prevalent activity recorded from randomly selected PFC neurons reflected the mathematical rules; purely sensory- and memory-related activity was almost absent. These data show that single PFC neurons have the capacity to represent flexible operations on most abstract numerical quantities. Our findings support PFC network models implementing specific "rule-coding" units that control the flow of information between segregated input, memory, and output layers. We speculate that these neuronal circuits in the monkey lateral PFC could readily have been adopted in the course of primate evolution for syntactic processing of numbers in formalized mathematical systems.
数学是基于如何构建、处理和评估数值信息的高度抽象原则或规则。然而，数学规则如何可以被单个神经元表示，这一点仍然难以捉摸。因此，我们记录了猕猴前额叶皮层（PFC）单个神经元的活动，这些神经元需要灵活地在“大于”和“小于”规则之间切换。猴子使用不同的数字数量完成此任务，并推广到之前未呈现的集合大小，表明它们已经学习了抽象的数学原理。从随机选择的 PFC 神经元中记录到的最常见活动反映了数学规则；几乎不存在纯粹的感官和记忆相关的活动。这些数据表明，单个 PFC 神经元具有表示对最抽象数值进行灵活操作的能力。我们的发现支持实现特定“规则编码”单元的 PFC 网络模型，这些单元控制信息在隔离的输入、记忆和输出层之间的流动。我们推测，在灵长类动物进化过程中，猴子外侧 PFC 中的这些神经元回路可能很容易被用于形式化数学系统中数字的句法处理。
