## 2 Related Work
It aims to exploit the advantages of bio-inspired neural components, which are sparse and event-driven, offering a fundamental difference from ANNs. It provides a modern and proven infrastructure based on PyTorch and deep learning-compatible SNN components. Also, it offers many neuron model implementations, such as the Leaky Integrate-and-Fire (LIF) model and Long Short-Term Memory SNNs (LSNN), which can be easily integrated into neural networks. The library solves two of the most complex parts of running neuron simulations: neural equations and temporal dynamics. It distinguishes between time and recurrence for each neuron model, allowing for flexible and efficient simulations. Norse supports various learning algorithms and plasticity models, including gradient-based optimization using PyTorch's built-in methods and biologically plausible learning rules like Spike-Timing-Dependent Plasticity (STDP). The library can be applied to both fundamental research and deep learning problems. Also, existing deep learning models can be ported to the spiking domain by replicating the architecture, lifting the signal in time, and replacing activation functions with spiking neurons. Lava v0.9.0 [29] is an open-source software library dedicated to developing algorithms for neuromorphic computation. It provides an easy-to-use Python interface for creating the components required for neuromorphic algorithms. One of Lava's key features is that it allows users to run and test all neuromorphic algorithms on standard von Neumann hardware, such as CPUs, before deploying them on neuromorphic processors, such as the Intel Loihi 1/2 processor. This enables developers to leverage the speed and energy efficiency of neuromorphic hardware. Lava is designed to be extensible, allowing for custom implementations of neuromorphic behavior and support for new hardware backends. It can be used at two levels: using existing resources to create complex algorithms with minimal neuromorphic knowledge or extending Lava with custom behavior defined in Python and C. The fundamental building block in the Lava architecture is the Process, which describes a functional group, such as a population of LIF neurons, that runs asynchronously and in parallel, communicating via Channels. Lava provides a growing Process Library containing many commonly used Processes and high-level functionalities for deep learning applications, constraint optimization, and Dynamic Neural Fields. The library is actively developed and maintained, focusing on providing a user-friendly and efficient framework for neuromorphic computing. The authors in [30] presented Sinabs Is Not A Brain Simulator (Sinabs v2.0.0), a deep learning library based on PyTorch for SNNs. This library focuses on simplicity, fast training, and extendability and works well for vision models because it supports weight transfer from Convolutional Neural Networks (CNNs) to SNNs. This feature allows for the identification and comparison of layers between CNNs and SNNs. The library provides tutorials to help users get started quickly, including working with neuromorphic versions of datasets like the MNIST. It also offers an API reference for more advanced usage and supports plugins for deploying models to neuromorphic hardware and training feed-forward models up to 10 times faster. The Python package Rockpool [31] is designed to work with dynamic neural networks, particularly SNNs, in contrast to standard ANNs. It allows users to design, simulate, train, and test these networks. The library provides a high-level interface for defining and simulating SNNs and tools for analyzing and visualizing the network's behavior. Rockpool supports various neuron and synapse models, enabling users to build and explore different types of SNNs. One of the critical features of Rockpool is its ability to interface with neuromorphic hardware devices, such as the SpiNNaker and BrainScaleS systems. Thisallows users to seamlessly transfer their simulated networks to physical hardware for more efficient computation.