Simen Patrick, Polk Thad
Princeton Neuroscience Institute Princeton University Princeton, NJ.
Log J IGPL. 2010 Oct 1;18(5):705-761. doi: 10.1093/jigpal/jzp046.
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.
研究复杂认知的人员越来越热衷于将符号认知架构映射到亚符号脑模型上。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，根本不涉及任何规则）之外，这种映射对于理解认知似乎至关重要。进行这种映射可简化为指定大脑活动的符号描述与亚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、结构化的循环神经网络。这些模型中的反馈强度决定了其组件是实现经典的亚符号神经网络功能（例如模式识别），还是实现逻辑规则和数字记忆。这些技术支持有限产生式系统的实现。虽然这些神经产生式系统本质上是顺序性和符号性的，但它们可以利用心理学和神经科学中决策模型的并行模拟处理原理来解释脑损伤对问题解决行为的影响。
