Parallel double-conversion spiking neural network for world-centered recognition

科研成果: 期刊稿件文章同行评审

1 引用 (Scopus)

摘要

The separation of patterns from ground is the necessary requirement for recognition. Most of neural recognition models are network-centered without the ability to extract patterns. As a result, some non-neural methods and the learning of pattern's variant positions are used to complete the task. This article presents a spiking double-conversion network (DCN) to search for patterns in input using the double conversions from the network-centered input vector to a time sequence and further from the sequence to pattern-centered vector. DCN is designed for network-centered recognition and cluster models to extend them to world-centered ones.

源语言英语
页(从-至)173-183
页数11
期刊Neurocomputing
30
1-4
DOI
出版状态已出版 - 2000

指纹

探究 'Parallel double-conversion spiking neural network for world-centered recognition' 的科研主题。它们共同构成独一无二的指纹。

引用此