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Pattern recognition using Spiking Neural Netwoks with temporal encoding and learning

机译:使用带有时间编码和学习的Spiking神经网络进行模式识别

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Pattern Recognition plays important role in several activities like speech recognition, face recognitions, character recognition. Patterns are recognized using Spiking Neural Networks. Commonly neural networks are used in analytical decision making process and cognitive process. Spiking neural network with leaky integrate fire neurons are used to recognize the patterns. Biologically inspired supervised learning is used to recognize the pattern. Temporal encoding, learning, and readout process is carried out during classifying the patterns. Spiking neural networks process different inputs and produce the accurate and fast recognition of the particular pattern. In this paper iris data set has to be taken to classifying those patterns. By using a temporal encoding learning process are used in iris data set which is effectively and efficiently recognizing the pattern.
机译:模式识别在语音识别,面部识别,字符识别等几个活动中起着重要作用。使用尖刺神经网络识别模式。通常神经网络用于分析决策过程和认知过程。使用漏漏的神经网络用于识别模式。生物启发的监督学习用于识别模式。在分类模式期间执行时间编码,学习和读出过程。尖峰神经网络处理不同的输入,并产生对特定模式的准确和快速识别。在本文中,必须采取虹膜数据集来分类这些模式。通过使用时间编码学习过程用于IRIS数据集,其有效且有效地识别模式。

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