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Updated: Apr 25, 2026
10:32
Design, Surface Treatment, Cellular Plating, and Culturing of Modular Neuronal Networks Composed of Functionally Inter-connected Circuits
Published on: April 15, 2015
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Mathematical modeling for evolution of heterogeneous modules in the brain
Yutaka Yamaguti1, Ichiro Tsuda1
1Research Institute for Electronic Science, Hokkaido University, Kita-12, Nishi-7, Kita-ku, Sapporo, Hokkaido 060-0812, Japan; Research Center for Integrative Mathematics, Hokkaido University, Kita-12, Nishi-7, Kita-ku, Sapporo, Hokkaido 060-0812, Japan.
Summary
Researchers evolved brain-like networks using a genetic algorithm to maximize information flow. This process led to the emergence of two distinct modules from initially uniform systems, offering insights into brain network evolution.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Evolutionary Biology
Background:
- Modular architecture is prevalent in mammalian brains, yet its evolutionary origins remain unclear.
- Maximizing information transmission is a proposed principle for complex brain network development.
Purpose of the Study:
- To investigate the evolutionary development of heterogeneous modules in coupled-map networks.
- To explore how maximizing bidirectional information transmission influences network structure.
Main Methods:
- Utilized a genetic algorithm for network evolution.
- Employed coupled-map networks with selection based on maximizing bidirectional information transmission.
Main Results:
- Two functionally differentiated modules evolved from initially homogeneous systems.
- Module emergence was linked to symmetry breaking in intra- and inter-system couplings.
- Optimal network configurations were found near transition points, preceding the emergence of slow oscillations.
Conclusions:
- Information transmission maximization can drive the evolution of modular brain network structures.
- Symmetry breaking plays a crucial role in functional differentiation of brain modules.
- Network dynamics near critical transitions are important for evolved complex brain architectures.
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