Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons
This work addresses a computational bottleneck for researchers in computational neuroscience, enabling faster and more accurate simulations of neural models, though it is incremental as it builds on existing ALIF methods.
The paper tackled the speed-accuracy trade-off in simulating adaptive leaky integrate-and-fire neurons by algorithmically reinterpreting the model to enable efficient GPU parallelization, achieving over a 50x training speedup with small discretization time-steps while maintaining comparable performance on classification tasks.
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.