Topological reinforcement as a principle of modularity emergence in brain networks
Modularity is a ubiquitous topological feature of structural brain networks at various scales. Although a variety of potential mechanisms have been proposed, the fundamental principles by which modularity emerges in neural networks remain elusive. We tackle this question with a plasticity model of n...
Main Authors: | Fabrizio Damicelli, Claus C. Hilgetag, Marc-Thorsten Hütt, Arnaud Messé |
---|---|
Format: | Article |
Language: | English |
Published: |
The MIT Press
2019-05-01
|
Series: | Network Neuroscience |
Subjects: | |
Online Access: | https://www.mitpressjournals.org/doi/pdf/10.1162/netn_a_00085 |
Similar Items
-
Action selection in modular reinforcement learning
by: Zhang, Ruohan
Published: (2014) -
Optimal hierarchical modular topologies for producing limited sustained activation of neural networks
by: Marcus Kaiser, et al.
Published: (2010-05-01) -
Topology Optimization of Metal and Carbon Fiber Reinforced Plastic (CFRP) Laminated Battery-Hanging Structure
by: Jiaju Chen, et al.
Published: (2020-10-01) -
Computational Design of Modular Robots Based on Genetic Algorithm and Reinforcement Learning
by: Jai Hoon Park, et al.
Published: (2021-03-01) -
Topology optimization of reinforced concrete beams by a spread-over reinforcement model with fixed grid mesh
by: Benjapon Wethyavivorn, et al.
Published: (2011-02-01)