机械符号的熵联想记忆
Entropic Associative Memory for Manuscript Symbols
摘要：
Mannapt符号可以存储，识别和检索从一个熵的数字记忆，是关联的和分布式的，但还声明;记忆检索是一个建设性的操作，内存中不包含的对象的记忆线索被拒绝直接搜索，和内存操作可以通过并行计算执行。字母和数字这两种Mandatopt符号都表示在具有关联熵的关联存储寄存器中。记忆识别操作遵循精确度和召回率之间的熵权衡，并且熵水平影响通过记忆检索操作恢复的对象的质量。本建议在几个方面与关联记忆的神经网络模型进行了对比。我们讨论的熵联想记忆检索对象的完整和不完整的信息，如严重闭塞的操作特性。本文中报道的实验增加了证据的潜力，这一框架的开发实际应用和自然记忆的计算模型。
英文摘要：
Manuscript symbols can be stored, recognized and retrieved from an entropic digital memory that is associative and distributed but yet declarative; memory retrieval is a constructive operation, memory cues to objects not contained in the memory are rejected directly without search, and memory operations can be performed through parallel computations. Manuscript symbols, both letters and numerals, are represented in Associative Memory Registers that have an associated entropy. The memory recognition operation obeys an entropy trade-off between precision and recall, and the entropy level impacts on the quality of the objects recovered through the memory retrieval operation. The present proposal is contrasted in several dimensions with neural networks models of associative memory. We discuss the operational characteristics of the entropic associative memory for retrieving objects with both complete and incomplete information, such as severe occlusions. The experiments reported in this paper add evidence on the potential of this framework for developing practical applications and computational models of natural memory.
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