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Updated: Jun 26, 2026
06:28
Standardized Modular Assembly of Polycistronic Operons with Modular Cloning (MoClo) using the In-Cloning toolkit
Published on: September 2, 2025
Modular network SOM.
Kazuhiro Tokunaga1, Tetsuo Furukawa
1Department of Brain Science and Engineering, Kyushu Institute of Technology, Kitakyushu, Japan.
Summary
This study introduces the modular network SOM (mnSOM), a novel framework using trainable neural networks as modules. This approach maps functions in function space, enhancing self-organizing map capabilities.
Area of Science:
- Artificial Intelligence
- Machine Learning
- Neural Networks
Background:
- Conventional Self-Organizing Maps (SOMs) utilize vector units for data representation.
- There is a need for advanced SOM architectures capable of handling complex functional relationships.
Purpose of the Study:
- To develop a generalized framework for a modular network SOM (mnSOM).
- To introduce an mnSOM architecture utilizing trainable neural networks, such as Multi-Layer Perceptrons (MLPs), as functional modules.
- To demonstrate the capability of mnSOMs to operate in function space.
Main Methods:
- The study proposes an array structure composed of functional modules (e.g., MLPs) instead of conventional vector units.
- An algorithm is introduced for the MLP-module-based mnSOM.
- A generalized framework for mnSOM is described.
Main Results:
- The MLP-module-mnSOM learns groups of systems or functions by analyzing input-output relationships.
- The mnSOM generates a feature map representing these functions, operating in function space.
- Simulation results for the MLP-module-mnSOM are presented.
Conclusions:
- The modular network SOM (mnSOM) offers a generalized and powerful framework for function space mapping.
- mnSOMs with MLP modules represent a significant advancement over traditional SOMs for complex system analysis.
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