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A neuromorphic paradigm for extrinsically evolved hybrid analog/digital device controllers: initial explorations

机译:用于外部进化的混合式模拟/数字设备控制器的神经形态范式:初始探索

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This paper argues that the continuous time recurrent neural network (CTRKN) provides a particularly attractive paradigm for the extrinsic evolution of analog device controllers. The paper begins with a discussion of motivations and difficulties faced in evolving electrical circuits and then illustrates how some of these difficulties have been successfully addressed in the context of evolved CTRNNs. Following will be a presentation of a new, hardware friendly, CTRNN formulation as well as some preliminary experimental results demonstrating that practical devices can be evolved under the new model. Finally, the paper will conclude with a discussion of open issues and a summary of current plans to close those gaps.
机译:本文认为,连续时间复发性神经网络(CTRKN)为模拟装置控制器的外在演进提供了特别有吸引力的范例。本文始于对演化电路的动机和困难的讨论,然后说明在演进的CTRNN的背景下已经成功地解决了这些困难的困难。以下将是一个新的,硬件友好,Ctrnn制定的演示以及一些初步的实验结果,证明了在新模型下可以进化实际设备。最后,本文将讨论开放问题,并概述当前计划关闭这些差距。

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