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Can computational efficiency alone drive the evolution of modularity in neural networks?
1School of Biology, Newcastle University, Ridley Building 2, Newcastle upon Tyne NE1 7RU, UK.
Scientific Reports
|August 31, 2016
Summary
Computational efficiency alone does not drive the evolution of modularity in biological networks. However, non-gradualistic evolution, such as duplication, may allow modularity to emerge.
Area of Science:
- Evolutionary biology
- Computational neuroscience
- Network science
Background:
- The role of computational efficiency in the evolution of biological network modularity is debated.
- Previous research suggests small modular networks are inefficient, while large ones show slight efficiency gains over non-modular networks.
Purpose of the Study:
- To investigate if computational efficiency advantages in larger networks can drive the evolution of modularity.
- To explore the evolutionary pathways leading to modularity in evolving connective architectures.
Main Methods:
- Simulations of gradualistic connective evolution across various parameter states.
- Simulations incorporating non-gradualistic evolutionary mechanisms, specifically architectural duplication.
Main Results:
- Gradualistic evolution consistently resulted in non-modular network structures (local attractors).
- A high-performance modular network state was identified but found unreachable through gradualistic evolution.
- Non-gradualistic evolution, utilizing duplication, demonstrated a viable pathway to achieving multi-modularity.
Conclusions:
- Computational efficiency alone is insufficient to drive the evolution of modularity in biological networks, even large ones.
- Modularity may evolve through non-gradualistic processes like duplication, where efficiency can play a role.
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