Dynamical complexity and computation in recurrent neural networks beyond their fixed point.
Fiche publication
Date publication
février 2018
Journal
Scientific reports
Auteurs
Membres identifiés du Cancéropôle Est :
Dr LARGER Laurent
Tous les auteurs :
Marquez BA, Larger L, Jacquot M, Chembo YK, Brunner D
Lien Pubmed
Résumé
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.
Référence
Sci Rep. 2018 Feb 20;8(1):3319