## 1 Introduction  
Spiking Neural Networks (SNNs) are Artificial Neural Networks (ANNs) that more closely mimic the biological neural networks found in the brain [1]. Unlike traditional ANNs, SNNs use spikes or pulses to transmit information, similar to how neurons in the brain communicate [2, 3]. The importance of SNNs lies in their potential to achieve energy efficiency and fast inference, making them well-suited for applications like edge computing and real-time processing [4, 5]. By leveraging the temporal dynamics of spikes, SNNs can encode and process information more efficiently than traditional neural networks. Some key advantages of SNNs include (a) energy efficiency: SNNs have the potential to be more energy efficient than traditional neural networks due to their event-driven nature and sparse activity [6, 7]; (b) fast inference: The asynchronous and parallel processing capabilities of SNNs enable fast inference, making them suitable for real-time applications [8]; (c) biological plausibility: SNNs more closely resemble the functioning of biological neural networks, providing insights into brain function and enabling the development of brain-inspired computing systems [9]; (d) temporal processing: SNNs can naturally process and encode temporal information, making them well-suited for tasks involving time-series data or temporal patterns [10]. Despite their advantages, SNNs face challenges, such as the lack of standardized training algorithms and difficulty designing efficient architectures [11]. Designing and understanding SNN architectures can be challenging because SNNs are more complex than traditional ANNs, incorporating temporal dynamics and spike-based communication. This added complexity makes it difficult to design efficient and effective architectures. Unlike traditional neural networks, there are no well-established design principles or best practices for constructing SNN architectures. This lack of standardization makes it challenging for researchers to determine the optimal architecture for a given task [12]. Also, SNNs are inspired by biological neural networks, but the understanding of how the brain processes information is still limited. This incomplete knowledge makes designing SNN architectures that accurately mimic brain function difficult. Due to their complex temporal dynamics and spike-based communication, SNNs can also be challenging to visualize and interpret [13]. Therefore, traditional visualization techniques used for ANNs may not be sufficient for understanding the behavior of SNNs. Moreover, SNNs can be computationally expensive to simulate and train, especially when dealing with large-scale networks. This computational complexity can limit the ability to explore and optimize different architectures [14]. Also, user-friendly tools for designing, training, and visualizing SNN models are found in a small number in the literature. This absence of a variety of accessible modern tools makes it challenging for researchers to experiment with different architectures and gain insights into their behavior [15; 16; 17]. The relatively small number of user-friendly SNN training and visualization tools is a significant challenge in neuromorphic computing. Despite the potential advantages of SNNs, such as energy efficiency and biological plausibility, the absence of accessible tools hinders their widespread adoption and understanding. Most existing tools for training and visualizing SNNs often require extensive technical knowledge and programming skills, making them inaccessible to researchers and practitioners. This absence of enough user-friendly tools can lead to several issues. Without easy-to-use tools, researchers may be discouraged from exploring different SNN architectures and hyperparameters, hindering the discovery of optimal models for specific tasks. SNNs can be complex and challenging to interpret without proper visualization tools. The absence of intuitive visualizations makes gaining insights into the network's behavior and identifying potential issues difficult.