文章基本信息

标题：Dynamical complexity and computation in recurrent neural networks beyond their fixed point
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作者：Bicky A. Marquez ; Laurent Larger ; Maxime Jacquot 等
期刊名称：Scientific Reports
电子版ISSN：2045-2322
出版年度：2018
卷号：8
期号：1
页码：3319
DOI：10.1038/s41598-018-21624-2
语种：English
出版社：Springer Nature
摘要：Spontaneous activity found in neural networks usually results in a reduction of computational performance. As a consequence, artificial neural networks are often operated at the edge of chaos, where the network is stable yet highly susceptible to input information. Surprisingly, regular spontaneous dynamics in Neural Networks beyond their resting state possess a high degree of spatio-temporal synchronization, a situation that can also be found in biological neural networks. Characterizing information preservation via complexity indices, we show how spatial synchronization allows rRNNs to reduce the negative impact of regular spontaneous dynamics on their computational performance.