Structural Level Comparison of two Basic Paradigms in Neural Computation

机译：神经计算中两个基本范式的结构水平比较

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The two more known paradigms of Neural Networks are usually considered as very different structures. In this paper both structures are studied from the point of view of a general formal hierarchical recursive framework for describing connectionist models. As a result of this study both paradigms are defined, using the same language, from a top level of abstraction, down to a level suitable for implementation. The final basic primitives of the descriptions are taken from a set of standard building blocks in computation.

机译：最着名的神经网络的范式通常被认为是非常不同的结构。本文从一般的正式分层递归框架的角度来看，两个结构都研究了用于描述连接主义模型的。由于本研究，两个范例都使用相同的语言从顶层抽象，下降到适合实现的级别。描述的最终基本原语来自计算中的一组标准构建块。

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《International work-conference on artificial and natural neural networks》|1997年||共11页
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Jose R. Alvarez;
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中图分类神经生理学;
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Structural Level Comparison of two Basic Paradigms in Neural Computation

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