SNNtrainer3D: Training Spiking Neural Networks using a User-friendly Application with 3D Architecture Visualization Capabilities

Today, exactly 5 months since I started working as a neuromorphic computing researcher at pmTUC, my first research paper in this field, „SNNtrainer3D: Training Spiking Neural Networks using a User-friendly Application with 3D Architecture Visualization Capabilities“ was published in the MDPI’s Applied Sciences journal.

Abstract: Spiking Neural Networks have gained significant attention due to their potential for energy efficiency and biological plausibility. However, the reduced number of user-friendly tools for designing, training, and visualizing Spiking Neural Networks hinders widespread adoption. This paper presents the SNNtrainer3D v1.0.0, a novel software application that addresses these challenges. The application provides an intuitive interface for designing Spiking Neural Networks architectures, with features such as dynamic architecture editing, allowing users to add, remove, and edit hidden layers in real-time. A key innovation is the integration of Three.js for three-dimensional visualization of the network structure, enabling users to inspect connections and weights and facilitating a deeper understanding of the model’s behavior. The application supports training on the Modified National Institute of Standards and Technology dataset and allows the downloading of trained weights for further use. Moreover, it lays the groundwork for future integration with physical memristor technology, positioning it as a crucial tool for advancing neuromorphic computing research. The advantages of the development process, technology stack, and visualization are discussed. The SNNtrainer3D represents a significant step in making Spiking Neural Networks more accessible, understandable, and easier for Artificial Intelligence researchers and practitioners.

The entire SNNtrainer3D code and additional implementation and experimental files can be found on GitHub here:

PS. The SNNtrainer3D is improved constantly. It now supports multiple datasets, extra neurons and features, and more.

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