Abstract：
: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.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Marquez BA,Larger L,Jacquot M,Chembo YK,Brunner Ddoi
10.1038/s41598-018-21624-2subject
Has Abstractpub_date
2018-02-20 00:00:00pages
3319issue
1issn
2045-2322pii
10.1038/s41598-018-21624-2journal_volume
8pub_type
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