Researchers studying complex cognition have grown increasingly interested in mapping symbolic cognitive architectures onto subsymbolic brain models. Such a mapping seems essential for understanding cognition under all but the most extreme viewpoints (namely, that cognition consists exclusively of digitally implemented rules; or instead, involves no rules whatsoever). Making this mapping reduces to specifying an interface between symbolic and subsymbolic descriptions of brain activity. To that end, we propose parameterization techniques for building cognitive models as programmable, structured, recurrent neural networks. Feedback strength in these models determines whether their components implement classically subsymbolic neural network functions (e.g., pattern recognition), or instead, logical rules and digital memory. These techniques support the implementation of limited production systems. Though inherently sequential and symbolic, these neural production systems can exploit principles of parallel, analog processing from decision-making models in psychology and neuroscience to explain the effects of brain damage on problem solving behavior.

中文翻译：

---

用于认知建模的符号/子符号接口协议。

研究复杂认知的研究人员对将符号认知架构映射到亚符号大脑模型越来越感兴趣。除了最极端的观点（即，认知完全由数字实现的规则组成；或者相反，不涉及任何规则），这种映射对于理解认知似乎是必不可少的。进行这种映射简化为指定大脑活动的符号和子符号描述之间的接口。为此，我们提出了参数化技术，用于将认知模型构建为可编程、结构化、循环神经网络。这些模型中的反馈强度决定了它们的组件是实现经典的子符号神经网络功能（例如，模式识别），还是逻辑规则和数字记忆。这些技术支持有限生产系统的实施。尽管本质上是顺序和象征性的，但这些神经生产系统可以利用来自心理学和神经科学决策模型的并行模拟处理原理来解释脑损伤对解决问题行为的影响。