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Time ordering in the evolution of information processing and modulation systems

机译:信息处理和调制系统演进中的时间顺序

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摘要

The ideas of optimization of learning algorithms in artificial neural networks are reviewed emphasizing generic properties; and the on-line implementations are interpreted from a biological perspective. A simple model of the relevant subsidiary variables needed to improve learning in artificial feedforward networks and the 'time ordering' of the appearance of the respective information processing systems is proposed. We discuss the possibility that these results might be relevant in other contexts, not being restricted to the simple models from which they stem. The analysis of a few examples, which range from the lowest cellular scale to the macroscopic level, suggests that similar ideas could be applied to biological systems. [References: 34]
机译:回顾了人工神经网络中学习算法优化的思想,强调了通用属性;从生物学的角度解释在线实施。提出了一个相关辅助变量的简单模型,该模型需要改进人工前馈网络中的学习以及各个信息处理系统的外观的“时间顺序”。我们讨论了这些结果在其他情况下可能相关的可能性,而不仅限于它们所源自的简单模型。对一些示例的分析(范围从最低的细胞规模到宏观水平)表明,类似的想法可以应用于生物系统。 [参考:34]

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