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Beyond bumps: Spiking networks that store sets of functions

机译:超越障碍:存储功能集的尖峰网络

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There are currently a number of models that use spiking neurons in recurrent networks to encode a stable Gaussian 'bump' of activation. These models successfully capture some behaviors of various neural systems (e.g., storing a single spatial location in working memory). However, they are limited to encoding single bumps of uuiform height. We extend this previous Work by showing how to construct and analyze realistic spiking networks that encode multiple 'bumps' of different heights. Our networks capture additional experimentally observed behav- ior (e.g., storing multiple spatial locations at the same time and the sensitivity of working memory to non-spatial parameters).
机译:当前,有许多模型在循环网络中使用尖峰神经元来编码稳定的高斯激活“凸点”。这些模型成功捕获了各种神经系统的某些行为(例如,将单个空间位置存储在工作内存中)。但是,它们仅限于编码uuiform高度的单个凹凸。我们通过展示如何构建和分析编码不同高度的多个“凸点”的逼真的尖峰网络来扩展之前的工作。我们的网络捕获了其他实验观察到的行为(例如,同时存储多个空间位置以及工作记忆对非空间参数的敏感性)。

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