Spiking neural network simulations are a central tool in Computational Neuroscience, Artificial Intelligence, and Neuromorphic Engineering research. A broad range of simulators and software frameworks for such simulations exist with different target application areas. Among these, PymoNNto is a recent Python-based toolbox for spiking neural network simulations that emphasizes the embedding of custom code in a modular and flexible way. While PymoNNto already supports GPU implementations, its backend relies on NumPy operations. Here we introduce PymoNNtorch, which is natively implemented with PyTorch while retaining PymoNNto's modular design. Furthermore, we demonstrate how changes to the implementations of common network operations in combination with PymoNNtorch's native GPU support can offer speed-up over conventional simulators like NEST, ANNarchy, and Brian 2 in certain situations. Overall, we show how PymoNNto's modular and flexible design in combination with PymoNNtorch's GPU acceleration and optimized indexing operations facilitate research and development of spiking neural networks in the Python programming language.

中文翻译：

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使用 PymoNNto 和 PymoNNtorch 加速尖峰神经网络模拟

尖峰神经网络模拟是计算神经科学、人工智能和神经形态工程研究的核心工具。针对不同的目标应用领域，存在用于此类模拟的各种模拟器和软件框架。其中，PymoNNto 是一个最新的基于 Python 的工具箱，用于尖峰神经网络模拟，强调以模块化和灵活的方式嵌入自定义代码。虽然 PymoNNto 已经支持 GPU 实现，但其后端依赖于 NumPy 操作。这里我们介绍 PymoNNtorch，它是用 PyTorch 原生实现的，同时保留了 PymoNNto 的模块化设计。此外，我们还演示了如何将常见网络操作的实现更改与 PymoNNtorch 的本机 GPU 支持相结合，在某些情况下可以比 NEST、ANNarchy 和 Brian 2 等传统模拟器提供加速。总的来说，我们展示了 PymoNNto 的模块化和灵活的设计与 PymoNNtorch 的 GPU 加速和优化的索引操作相结合如何促进使用 Python 编程语言进行尖峰神经网络的研究和开发。