Attractor Dynamics Driven by Interactivity in Boolean Recurrent Neural Networks

被引：4

作者：

Cabessa, Jeremie ^{[1
]}

Villa, Alessandro E. P. ^{[2
]}

机构：

[1] Univ Pantheon Assas Paris 2, Lab Math Econ LEMMA, 4 Rue Blaise Desgoffe, F-75006 Paris, France

[2] Univ Lausanne, Neuroheurist Res Grp, Dept Informat Syst, CH-1015 Lausanne, Switzerland

来源：

ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT I | 2016年 / 9886卷

关键词：

SEROTONERGIC MODULATION; NEURONS; HYPOTHALAMUS; PATTERNS;

D O I：

10.1007/978-3-319-44778-0_14

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We study the attractor dynamics of a Boolean model of the basal ganglia-thalamocortical network as a function of its interactive synaptic connections and global threshold. We show that the regulation of the interactive feedback and global threshold are significantly involved in the maintenance and robustness of the attractor basin. These results support the hypothesis that, beyond mere structural architecture, global plasticity and interactivity play a crucial role in the computational and dynamical capabilities of biological neural networks.

引用

页码：115 / 122

页数：8

共 27 条

[1] DETECTING SPATIOTEMPORAL FIRING PATTERNS AMONG SIMULTANEOUSLY RECORDED SINGLE NEURONS [J].