## 2 Related Work  
As summarized in Table 1, the literature contains several existing tools for simulating SNNs, each with its focus and features. The authors in [16] propose the Run-Time Analysis and Visualization Simulator (RAVSim), which is a software tool that enables researchers and developers to analyze and visualize SNNs in real time. It provides a user-friendly environment for experimenting with SNNs and understanding their behavior and performance. The current version, RAVSim v2.0, offers a standalone simulator for interactive exploration of SNN models without requiring additional software. Its key features include real-time analysis and visualization, which allows users to gain insights into the network's behavior as the simulation progresses. RAVSim has been used to evaluate the performance of SNN-based image classification models, helping researchers assess and optimize their algorithms. A community edition of RAVSim is available from National Instruments (NI) for noncommercial and nonacademic use, subject to usage policies and licensing requirements. The authors in [20] propose Nengo v4.0.0, a flexible and powerful software package for simulating large-scale SNNs. It offers a Python-based environment for building, testing, and deploying spiking and non-SNNs. Users can develop models using a Graphical User Interface (GUI)-based environment (NengoCUI v0.5.0) or a fully scriptable Python API. Nengo is highly extensible, allowing users to define custom neuron types, learning rules, optimization methods, and reusable subnetworks. The simulator is designed to efficiently solve problems using various hardware, including CPUs, GPUs, and neuromorphic hardware. Nengo has been used for many applications, such as deep learning, vision, motor control, working memory, and problem-solving. Nengo-DL, an extension of Nengo, integrates with the deep learning library TensorFlow, enabling users to optimize SNNs using gradient-based methods and leverage deep learning techniques. The core of Nengo's simulation functionality is the \"Simulator\" class, which serves as the base class for all Nengo simulators and provides methods for running simulations, accessing model data, and manipulating model parameters. The work in [21] presents the NEural Simulation Tool (NEST), a widely used simulator for large-scale SNNs. It focuses on the dynamics, size, and structure of neural systems rather than the detailed morphological and biophysical properties of individual neurons. NEST has been applied to various SNN models, including sensory processing, network activity dynamics, spike-synchronization, learning, and plasticity. It provides a Python interface called PyNEST and supports the simulator-independent Python interface PyNN. The simulator has a modular architecture that allows users to extend it by adding new modelsfor neurons, synapses, and devices. NEST is optimized for simulating large networks of spiking neurons and can efficiently utilize multiprocessor computers and computer clusters, demonstrating better than linear scaling with the number of processors. Also, Brian 2 [22] is a flexible and user-friendly simulator for SNNs written in Python. It is designed to be easy to learn and use, adaptable, and extensible. Users can define models by directly writing equations in mathematical notation, making the model descriptions concise and readable. Brian 2 generates optimized code specific to each model, ensuring high performance across various models. The simulator supports different neural models, including rate-based, spiking, and multi-compartmental models. It also allows for the definition of custom neuron and synapse models. Brian 2 is a clock-driven simulator, meaning that the simulation time is divided into equally spaced time steps, and the state variables of neurons and synapses are updated at each step according to the defined equations. The modular architecture of Brian 2 enables easy extension and integration with other tools, and it has a growing ecosystem of third-party packages that build upon its functionality.