Center for Systems Neuroscience, Boston University, 610 Commonwealth Ave, Boston, MA 02215, United States.
Neurobiol Learn Mem. 2021 Dec;186:107552. doi: 10.1016/j.nlm.2021.107552. Epub 2021 Nov 8.
The ability to use symbols is a defining feature of human intelligence. However, neuroscience has yet to explain the fundamental neural circuit mechanisms for flexibly representing and manipulating abstract concepts. This article will review the research on neural models for symbolic processing. The review first focuses on the question of how symbols could possibly be represented in neural circuits. The review then addresses how neural symbolic representations could be flexibly combined to meet a wide range of reasoning demands. Finally, the review assesses the research on program synthesis and proposes that the most flexible neural representation of symbolic processing would involve the capacity to rapidly synthesize neural operations analogous to lambda calculus to solve complex cognitive tasks.
使用符号的能力是人类智力的一个决定性特征。然而，神经科学尚未解释灵活表示和操作抽象概念的基本神经回路机制。本文将回顾符号处理的神经模型研究。综述首先关注符号如何在神经回路中表示的问题。然后，综述讨论了如何灵活组合神经符号表示以满足各种推理需求。最后，综述评估了程序综合的研究，并提出了符号处理的最灵活的神经表示将涉及快速综合类似于 lambda 演算的神经操作的能力，以解决复杂的认知任务。
