Identification of feedback loops in neural networks based on multi-step Granger causality | Zendy

Chaoyi Dong | Zendy; Dongkwan Shin | Zendy; Sunghoon Joo | Zendy; Yoonkey Nam | Zendy; KwangHyun Cho | Zendy

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Identification of feedback loops in neural networks based on multi-step Granger causality

Author(s) -

Chaoyi Dong,

Dongkwan Shin,

Sunghoon Joo,

Yoonkey Nam,

KwangHyun Cho

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts354

Subject(s) - bursting , granger causality , feedback loop , artificial neural network , negative feedback , computer science , identification (biology) , control theory (sociology) , positive feedback , biological neural network , oscillation (cell signaling) , biological system , artificial intelligence , biology , neuroscience , machine learning , physics , engineering , voltage , botany , computer security , control (management) , quantum mechanics , electrical engineering , genetics

Feedback circuits are crucial network motifs, ubiquitously found in many intra- and inter-cellular regulatory networks, and also act as basic building blocks for inducing synchronized bursting behaviors in neural network dynamics. Therefore, the system-level identification of feedback circuits using time-series measurements is critical to understand the underlying regulatory mechanism of synchronized bursting behaviors.

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