## 4 Experimental Setup and Results  
The experiments realized with the proposed application are presented below. They were done on a Lenovo ThinkPad laptop with Windows 10, 8 GB of RAM, and an Intel Core i5 8 th-generation microprocessor. It is important to mention that anyone can replicate such experiments without requiring expensive hardware equipment or a dedicated GPU, as snnTorch [27] supports efficient training of SNNs on the CPU thanks to the seamless integration with PyTorch, which takes care of all the CPU-based tensor computations. Figure 9: A summarized view of the entire training process for an SNN model trained with the proposed SNNtrainer3D application, starting with the data preparation and ending in the training loop. The proposed application is evaluated by exploring different SNN architectures. More precisely, all experiments run on one hidden layer, with 10, 15, 20, 25, 30, 50, 80, and 100 neurons, each training with all four types of neuron models: LIF, Lapicque, Synaptic, and Alpha. There are more ideal hyperparameters for training SNNs, such as 100-200 epochs to achieve good accuracy on MNIST, with an initial learning rate of 0.01 and 0.1, then decaying it over time, and a number of steps of 100 or more [27]. However, these hyperparameters for each training are maintained: Number of epochs = 1, Learning rate = 0.0005, Beta = 0.95, Number of steps = 25, and Batch size = 128. It was decided not to change them because the resulting SNN models achieved good accuracy and loss results regarding training and testing on the MNIST dataset, proving the proposed application's efficiency. Another reason is to minimize the time required for experiments; in the case of the experiments done here with the mentioned setup, each training takes around 3 min to complete. Regarding training and accuracy, Figures 10-13 show the analysis of how each neuron type influences the learning dynamics and performance of the SNN model. Here, SNN represents the trained SNN model; 1L represents one hidden layer; n represents the number of neurons; LIF represents the Leaky Integrate-and-Fire neurons; and Lapicque represents the Lapicque neurons, and syn represents the Synaptic neurons, and alfa represents the Alpha neurons. More precisely, as seen in Figure 10, the SNN models using LIF neurons show that configurations with more neurons tend to perform better in accuracy. Figure 11: Training and Testing Loss and Accuracy results after training different SNN architectures with the Lapicque neurons on the MNIST dataset. Figure 12: Training and Testing Loss and Accuracy results after training different SNN architectures with the Synaptic neurons on the MNIST dataset. Figure 11 indicates that the Lapicque neuron models seem to have a more pronounced discrepancy between training and testing accuracy than the LIF models, particularly for smaller amounts of neurons, indicating overfitting is more significant with this neuron type. Also, as seen in Figure 12, synaptic neuron models generally show good convergence, with the training and testing accuracy following similar trajectories. Here, SNN represents the trained SNN model; 1L represents one hidden layer; n represents the number of neurons. Here, the best-performing models also have more neurons, indicating that a more extensive capacity network benefits this neuron type. Finally, Figure 13 shows that the Alpha neuron models demonstrate strong performance with higher accuracy and less overfitting, particularly with more extensive networks. The convergence is smoother, and the gap between training and testing accuracy is narrower for most configurations, which indicates good generalization. Regarding training and testing loss, as seen in Figure 10, the loss decreases steadily for all models, with models containing more neurons exhibiting lower loss, which correlates with higher accuracy. However, the loss for the test set does not decrease as much for smaller networks, supporting the suggestion of overfitting. Figure 11 suggests a clear trend that more neurons lead to lower loss.