## 2 Related Work
The simulator also provides built-in tools for visualizing and analyzing simulation results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**SNN** & **Stanan & **Runtime** & **Requires** & **Platform Support** & **Development** & **Focus \& Key Features** \\
**Tools/Simulators** & **GUI** & **Output** & **Programming Skills** & **Platform Support** & **Language** & **Focus \& Key Features** \\ \hline Proposed & & & & & & & & Training and visualization of SNN architectures; dynamic editing; it fully allows changing the model architecture; multiple hidden layers; \\ SNNtrainer3D & Yes & Yes & No & CPU, GPU & Python, HTML, CSS, JavaScript using Three,\& Real-time analysis and visualization; user-friendly for education \\ RAVSim [16] & & & & & CPU, GPU, neuromorphic hardware & Python & Large-scale brain models; high-level model design abstractions \\ NEST [21] & & & Yes, Advanced & CPU & C++, Python & Large neural network simulations; efficient large network handling \\ Brian 2 [22] & & & Yes, Intermediate & CPU, GPU & Python & Flexible biological neural network simulations; expressive model definitions \\ SPIKE [23] & No & No & Yes, Advanced & C++ & Simulation of large-scale spiking networks; optimized performance \\ NEVESIM [24] & No & No & CPU & C++, Python & SNN modeling; detailed synaptic plasticity models \\ BindsNET [25] & & & Yes, Intermediate & & & & PyTorch integration \\ Spikinglely [26] & & & & & CPU, GPU & Neuromorphic computing; PyTorch-based; CUDA support \\ snnTorch [27] & & & & & Python & Deep learning with SNNs; PyTorch integration; gradient descent learning, neuromorphic computing, bio-inspired models, PyTorch-based \\ \multirow{2}{*}{Norse [28]} & & & & & & & Deep learning, neuromorphic computing, bio-inspired models, PyTorch-based \\  & & & & & CPU, GPU, neuromorphic & Neuromorphic computing, scalable architecture; supports various hardware. \\ \multirow{2}{*}{Lava [29]} & & & & & hardware & hardware. \\ \multirow{2}{*}{Sinabs [30]} & & & & & & & Deployment on neuromorphic hardware efficient mapping to hardware \\  & & & & & hardware & & \\ \multirow{2}{*}{Rockpool [31]} & & & & & & Training SNNs for neuromorphic hardware, training algorithms, hardware deployment \\ CARLsim [32] & & & & & & & Large-scale SNN simulations; GPU acceleration; detailed neuron models \\ Spyx [33] & & & Yes, Intermediate & CPU, GPU & Python & Fast prototyping of SNNs; speed and ease of use; various SNN models supported \\ \multirow{2}{*}{NESIM-RT [34]} & & & & & & & models is supported \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison between existent SNN simulators found in the literature. SPIKE [23] is a fast and efficient simulator for SNNs that handles large-scale SNN simulations. It is currently one of the fastest SNN simulators available, demonstrating superior run-time performance compared to other simulators. SPIKE leverages the power of GPUs to accelerate SNN simulations, significantly reducing the simulation time for large-scale networks. Its performance advantage becomes more pronounced as the size and complexity of the simulated networks increase, making it highly scalable. The primary goal of SPIKE is to provide fast and efficient simulations of SNNs, prioritizing simulation speed over extensive features or biological realism. This focus on speed enables rapid experimentation and exploration of large-scale networks. The authors in [24] propose the Neural EVEnt-based SIMulator (NEVESIM), an object-oriented framework for simulating networks of spiking neurons, developed in C++ with a Python interface. It is designed to be an easily-extendable, general-purpose tool for event-based neural simulations that is simple to use. NEVESIM employs an event-driven simulation scheme, focusing on routing, scheduling, and delivering events (spikes) emitted by the network elements.