The adaptive leaky integrate-and-fire (ALIF) model is fundamental within
computational neuroscience and has been instrumental in studying our brains
$\textit{in silico}$. Due to the sequential nature of simulating these neural
models, a commonly faced issue is the speed-accuracy trade-off: either
accurately simulate a neuron using a small discretisation time-step (DT), which
is slow, or more quickly simulate a neuron using a larger DT and incur a loss
in simulation accuracy. Here we provide a solution to this dilemma, by
algorithmically reinterpreting the ALIF model, reducing the sequential
simulation complexity and permitting a more efficient parallelisation on GPUs.
We computationally validate our implementation to obtain over a $50\times$
training speedup using small DTs on synthetic benchmarks. We also obtained a
comparable performance to the standard ALIF implementation on different
supervised classification tasks - yet in a fraction of the training time.
Lastly, we showcase how our model makes it possible to quickly and accurately
fit real electrophysiological recordings of cortical neurons, where very fine
sub-millisecond DTs are crucial for capturing exact spike timing.

通过算法重新解释自适应泄露积分 - 放电 (ALIF) 模型，我们提供了一种解决神经模型顺序仿真的速度和精确性之间权衡的方法，并实现了在 GPU 上更高效的并行化。在合成基准测试中，我们通过小时间间隔进行了计算验证，获得了超过 50 倍的训练加速度。我们还在不同的监督分类任务上获得了与标准 ALIF 实现相当的性能，但训练时间大大缩短。最后，我们展示了我们的模型如何能够快速而准确地拟合大脑皮层神经元的实际电生理记录，这里精确的微秒级时间间隔对于捕捉确切的尖峰时间至关重要。