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摘要翻译：
本文探讨了与人脑作为一个学习系统有关的四个基本问题。大脑如何才能学会（1）在心理上模拟不同的外部记忆辅助工具，(2)在原则上使用想象记忆辅助工具进行任何心理计算，(3)回忆真实的感觉和运动事件并合成组合数量的想象事件，(4)动态地改变其心理设置以匹配组合数量的上下文？我们基于人类大脑皮层以“非经典”方式处理符号信息的一般假设，对（1）-（4）提出了统一的答案。它不像传统的符号系统那样处理读/写存储器中的符号，而是处理动态存储器的状态，这些状态表示存储在长期存储器中的不可移动的符号结构的不同临时属性。该方法被形式化为E-machine的概念。直观地说，电子计算机是一个主要处理表示内存指针子集而不是指针本身的特征函数的系统。这种非经典符号范式是图灵普适的，与经典符号范式不同的是，它可以有效地在同构神经网络中实现，其时间调制拓扑上类似于新皮层的时间调制。
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英文标题：
《A nonclassical symbolic theory of working memory, mental computations,
and mental set》
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作者：
Victor Eliashberg
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最新提交年份：
2009
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分类信息：
一级分类：Computer Science 计算机科学
二级分类：Artificial Intelligence 人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science 计算机科学
二级分类：Neural and Evolutionary Computing 神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要：
The paper tackles four basic questions associated with human brain as a learning system. How can the brain learn to (1) mentally simulate different external memory aids, (2) perform, in principle, any mental computations using imaginary memory aids, (3) recall the real sensory and motor events and synthesize a combinatorial number of imaginary events, (4) dynamically change its mental set to match a combinatorial number of contexts? We propose a uniform answer to (1)-(4) based on the general postulate that the human neocortex processes symbolic information in a "nonclassical" way. Instead of manipulating symbols in a read/write memory, as the classical symbolic systems do, it manipulates the states of dynamical memory representing different temporary attributes of immovable symbolic structures stored in a long-term memory. The approach is formalized as the concept of E-machine. Intuitively, an E-machine is a system that deals mainly with characteristic functions representing subsets of memory pointers rather than the pointers themselves. This nonclassical symbolic paradigm is Turing universal, and, unlike the classical one, is efficiently implementable in homogeneous neural networks with temporal modulation topologically resembling that of the neocortex.
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PDF链接：
https://arxiv.org/pdf/0901.1152
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