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Dynamical responses of chaotic memory dynamics to weak input in a recurrent neural network model

机译:递归神经网络模型中混沌记忆动力学对弱输入的动力学响应

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Chaotic dynamics in a recurrent neural network model, in which limit cycle memory attractors are stored, is investigated by means of numerical methods. In particular, we focus on quick and sensitive response characteristics of chaotic memory dynamics to external input, which consists of part of an embedded memory attractor. We have calculated the correlation functions between the firing activities of neurons to understand the dynamical mechanisms of rapid responses. The results of the latter calculation show that quite strong correlations occur very quickly between almost all neurons within 1 ~ 2 updating steps after applying a partial input. They suggest that the existence of dynamical correlations or, in other words, transient correlations in chaos, play a very important role in quick and/or sensitive responses.
机译:通过数值方法研究了存储极限循环记忆吸引子的递归神经网络模型中的混沌动力学。特别是,我们专注于混沌记忆动力学对外部输入的快速和敏感的响应特性,该特性由一部分嵌入式记忆吸引子组成。我们已经计算了神经元激发活动之间的相关函数,以了解快速反应的动力学机制。后一计算结果表明,在施加部分输入后的1〜2个更新步骤中,几乎所有神经元之间都非常迅速地产生了很强的相关性。他们认为,动力学相关性的存在,换句话说,混沌中的瞬态相关性在快速和/或敏感的响应中起着非常重要的作用。

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