## 6 Conclusions  
This paper proposes SNNtrainer3D, a novel application that offers significant advantages by facilitating interactive learning and visualization of SNNs, enhancing understanding, and analyzing complex neural network behaviors. The use of Flask, PyTorch, and snnTorch, along with Three.js for 3D visualizations, ensures a powerful, user-friendly experience. These technologies combined allow for efficient development and prototyping, enabling researchers and developers to experiment with SNN architectures and dynamics visually, engagingly, and intuitively. Visualization plays a critical role by making the complex operations and structures of SNNs more understandable and accessible. It simplifies understanding of complex SNN architectures, enhances user experience, facilitates model design and debugs, and provides training in a user-friendly interface, addressing a significant need in the field. It also bridges the gap between abstract SNN concepts and practical understanding, allowing users to observe the effects of their adjustments in real-time directly. This not only aids in educational purposes but also enhances research capabilities by providing a visual feedback loop, which can lead to faster identification of patterns, behaviors, and potential areas for improvement in SNN designs. In the experiments, insights regarding model complexity, neuron type effectiveness, overfitting concerns, and training stability of SNNs were gathered. Increasing the number of neurons improves accuracy and loss, indicating that a more complex model can learn the MNIST dataset better. Also, Alpha neurons seem more effective for this task than the other neuron types, with a good balance between performance on the training set and generalization to the test set. There is also evidence of overfitting, particularly in simpler models with fewer neurons and in models with Lapicque neurons. This suggests that better regularization strategies or different training methodologies for these models are needed. Also, the Synaptic and Alpha neuron models seem to train more stably with smoother accuracy and loss curves, which might make these models more predictable and reliable in practical applications. This analysis can help future researchers decide which SNN architecture and neuron model might be the most effective for similar tasks and what modifications could improve the network's performance and generalizability. Therefore, the experiments suggest that the choice of neuron model in SNNs is critical and should be tailored to both the task and the available computational resources. While LIF neurons may be preferred for smaller, less complex tasks, Synaptic neurons may become more advantageous as network complexity increases. These findings also highlight the importance of network capacity in achieving high performance in SNNs, with diminishing returns observed beyond a certain threshold. Also, these insights could guide the development of more efficient and scalable neuromorphic systems [50], especially when creating neuromorphic 3D circuits [54] using printed memristors [53]. Despite the numerous advantages and innovations introduced by SNNtrainer3D, it is important to acknowledge the existing design principles established for SNN architectures. The Neural Engineering Framework (NEF) and the Semantic Pointer Architecture (SPA), as proposed by [PERSON] and colleagues [56; 57], offer biologically motivated design principles that are critical for the construction of functional SNNs. These frameworks provide structured methodologies that have been foundational in advancing the development and application of SNNs. Future work on SNNtrainer3D will explore integrating these established principles to enhance the tool's capability in designing biologically plausible and efficient SNN architectures. By incorporating NEF and SPA methodologies, we aim to provide a more comprehensive and robust platform for SNN research and development.