## 2 Related Work
### Hybrid Learning Platforms  
snnTorch [27] is a Python library integrating SNNs with PyTorch for gradient-based learning, leveraging PyTorch's capabilities and supporting efficient training, but it might not fully exploit the event-driven nature of SNNs. Spikingjelly [26] is an open-source framework for deep learning with SNNs based on PyTorch, providing a full-stack toolkit, fast training, and CUDA support, though it focuses on machine learning applications and may lack detailed biological modeling features. Norse [28] extends PyTorch for deep learning with bio-inspired SNN components, offering a modern infrastructure, flexibility, and support for various neuron models, but it involves complexity in handling neural equations and temporal dynamics. As can be seen, some of the limitations of most existing tools are that they require programming knowledge, making them inaccessible to researchers and practitioners without a solid computational background. They do not provide built-in visualization features for understanding SNN architectures and behavior, and therefore, users often need to rely on external tools or libraries for visualization. Also, some tools, particularly those focused on detailed biophysical models, may have a steep learning curve for users interested in higher-level SNN architectures and may not provide the flexibility to experiment with different SNN architectures or incorporate novel features easily. The SNNtrainer3D combines elements from these categories and aims to address these limitations by offering several unique advantages compared to the other SNN simulators presented earlier. First, it features dynamic architecture editing. Unlike most other SNN simulators, this tool enables users to add, remove, and modify hidden layers in real-time, providing flexibility in model experimentation and optimization. This is one of the most essential features, as it allows changing the model architecture by adding as many layers as the user desires. In contrast, existing alternative solutions discussed in the literature maintain a  
functions can be built from simpler functions. This flexibility supports the iterative design process, enabling users to experiment with and optimize network depth, which has been shown to enhance learning capacity and performance in various tasks. Second, 3D Visualization with Three.js r164: the software incorporates a novel 3D visualization of the model architecture using the Three.js library. This feature enhances model understanding by providing an interactive and intuitive representation of the network structure and connections. Users can zoom in, rotate, and move the visualization for better insights into the model's behavior. Other SNN simulators generally lack such advanced visualization capabilities. Third, user-friendly interface: The SNNtrainer3D offers a user-friendly interface for designing and training models. It simplifies the process of creating and modifying SNN architectures, making them accessible to researchers and practitioners with varying levels of expertise. Most other SNN simulators focus on functionality and performance, often at the expense of user experience. Fourth, integration with memristor technology: while not yet implemented, the software lays the groundwork for future integration with physical memristors. This feature is crucial for designing and optimizing physical neural networks built using memristors (e.g., neuromorphic circuits). By training and visualizing SNNs in software, researchers can better understand how to implement these networks using physical memristors. This integration is a unique aspect not commonly found in other SNN simulators. Fifth, a comprehensive solution: SNNtrainer3D provides a complete solution for designing, training, and visualizing SNN models. It combines the capabilities of model creation, dataset management, training, and visualization into a single, cohesive software package.