## 1 Introduction
As a result, this can slow down the progress of SNN research, as researchers spend more time on implementation details rather than focusing on the underlying principles and applications of SNNs. To address these challenges, there is a need for user-friendly tools that simplify the process of designing, training, visualizing, and evaluating SNN models. The SNNtrainer3D proposed in this paper aims to fill this gap by providing an intuitive interface for creating and training SNNs and a three-dimensional (3D) visualization of the network architecture using Three.js [18]. The application's dynamic architecture editing feature allows users to easily experiment with different network configurations, while the visualization capabilities facilitate a deeper understanding of the model's structure and behavior. The integration of Three.js for 3D visualization of network structures serves several purposes. First, it enhances users' ability to understand complex SNN architectures by providing an interactive, visual representation of the model. This visualization allows users to inspect connections and weights in a more intuitive manner compared to traditional two-dimensional (2D) representations. Second, the dynamic and interactive nature of Three.js enables real-time adjustments and visual feedback, which is crucial for the iterative design and debugging of SNN architectures. Finally, such advanced visualization tools can facilitate a deeper educational and conceptual understanding of how SNNs function, thus lowering the barrier to entry for researchers and practitioners new to this field. By providing a user-friendly tool for SNN training and visualization, this application has the potential to make SNNs more accessible to a broader audience and accelerate research in the field of neuromorphic computing, primarily due to its market share being expected to increase up to 20 billion dollars by 2035 [19]. The main contributions of this paper are:  
* Presenting SNNtrainer3D, a novel software application that simplifies the process of designing, training, and visualizing SNN models through an intuitive user interface that allows users to add, remove, and edit hidden layers in real-time, providing flexibility in model experimentation and optimization
* Integrating Three.js for an interactive 3D visualization of the SNN model architecture, enabling users to inspect connections and weights, facilitating a deeper understanding of the model's behavior
* Supporting training on the Modified National Institute of Standards and Technology (MNIST) dataset and allowing the downloading of trained weights for further use, making the application a comprehensive solution for SNN model development
* Conducting experiments to evaluate the performance of different SNN architectures and neuron types using the SNNtrainer3D application, providing insights into model complexity, neuron effectiveness, and training stability  
The paper is organized as follows: Section 2 presents the related work. Section 3 details the proposed solution regarding the SNNtrainer3D. Section 4 describes the experimental setup and results. Section 5 discusses the use of saved trained weights to create neuromorphic 3D circuits using printed memristors. Finally, Section 6 presents the conclusions of this paper.