Simulation speed matters for neuroscientific research: this includes not only how quickly the simulated model time of a large-scale spiking neuronal network progresses, but also how long it takes to instantiate the network model in computer memory. On the hardware side, acceleration via highly parallel GPUs is being increasingly utilized. On the software side, code generation approaches ensure highly optimized code, at the expense of repeated code regeneration and recompilation after modifications to the network model. Aiming for a greater flexibility with respect to iterative model changes, here we propose a new method for creating network connections interactively, dynamically, and directly in GPU memory through a set of commonly used high-level connection rules. We validate the simulation performance with both consumer and data center GPUs on two neuroscientifically relevant models: a cortical microcircuit of about 77,000 leaky-integrate-and-fire neuron models and 300 million static synapses, and a two-population network recurrently connected using a variety of connection rules. With our proposed ad hoc network instantiation, both network construction and simulation times are comparable or shorter than those obtained with other state-of-the-art simulation technologies, while still meeting the flexibility demands of explorative network modeling.

翻译：仿真速度对于神经科学研究至关重要：这不仅涉及大规模脉冲神经元网络在模拟时间上的推进速度，还涉及将网络模型实例化到计算机内存所需的时间。在硬件方面，通过高度并行的GPU进行加速正日益普及；在软件方面，代码生成方法通过牺牲修改网络模型后重复重新生成和编译代码的代价来确保高度优化的代码。为追求迭代模型修改时的更高灵活性，本文提出一种新方法：通过一组常用的高级连接规则，在GPU内存中交互式、动态且直接地创建网络连接。我们使用消费级和数据中心级GPU，在两种具有神经科学意义的模型上验证了仿真性能：一个包含约77,000个漏电积分点火神经元模型和3亿静态突触的皮层微回路，以及一个使用多种连接规则递归连接的双群体网络。通过本文提出的即时网络实例化方法，网络构建和仿真时间均与现有先进仿真技术相当或更短，同时满足了探索性网络建模对灵活性的需求。