Feedforward Networks: Adaptation, Feedback, and Synchrony

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Manuela Aguiar (Author) FEP; dias, aps (Author) FCUP; Field, M (Author) Other

Journal

Title: Journal of Nonlinear Science

ISSN: 0938-8974

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 7 Citations

Other information

Authenticus ID: P-00P-YNH

DOI: 10.1007/s00332-018-9513-7

Abstract (EN): We investigate the effect on synchrony of adding feedback loops and adaptation to a large class of feedforward networks. We obtain relatively complete results on synchrony for identical cell networks with additive input structure and feedback from the final to the initial layer of the network. These results extend the previous work on synchrony in feedforward networks by Aguiar et al. (Chaos 27:013103, 2017). We also describe additive and multiplicative adaptation schemes that are synchrony preserving and briefly comment on dynamical protocols for running the feedforward network that relates to unsupervised learning in neural nets and neuroscience. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.

Language: English

Type (Professor's evaluation): Scientific