设计具有鲁棒性和可重构计算的峰值神经网络

Georg Börner, Fabio Schittler Neves, M. Timme
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引用次数: 0

摘要

尖峰神经元网络构成了能够有效和弹性计算的模拟系统。最近的研究表明,对称连接的抑制性神经元网络可以实现基本的计算,使它们对系统中断具有弹性。例如,如果一个神经元的功能丢失(例如,神经元及其连接被移除),系统可以通过仅适应一个全局系统参数来鲁棒地重新配置。如何有效地调整网络参数以鲁棒地执行给定的计算仍然不清楚。在这里,我们提出了一种解析方法来推导这些参数。具体来说,我们分析了k-赢家通吃(k- wta)计算,从总共N个输入信号中识别k个最大信号的基本计算任务,可以从中构建任何计算。我们识别和描述了不同的动态机制,并提供了不同数量k的赢家之间的转换作为输入和网络参数的函数的解析表达式。因此,我们的研究结果提供了关于k-赢者通吃功能背后的动力学的分析见解,以及设计实现中断弹性动力学的尖峰神经网络计算系统的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Designing spiking neural networks for robust and reconfigurable computation
Networks of spiking neurons constitute analog systems capable of effective and resilient computing. Recent work has shown that networks of symmetrically connected inhibitory neurons may implement basic computations such that they are resilient to system disruption. For instance, if the functionality of one neuron is lost (e.g., the neuron, along with its connections, is removed), the system may be robustly reconfigured by adapting only one global system parameter. How to effectively adapt network parameters to robustly perform a given computation is still unclear. Here, we present an analytical approach to derive such parameters. Specifically, we analyze k-winners-takes-all (k-WTA) computations, basic computational tasks of identifying the k largest signals from a total of N input signals from which one can construct any computation. We identify and characterize different dynamical regimes and provide analytical expressions for the transitions between different numbers k of winners as a function of both input and network parameters. Our results thereby provide analytical insights about the dynamics underlying k-winner-takes-all functionality as well as an effective way of designing spiking neural network computing systems implementing disruption-resilient dynamics.
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