Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons

Conference item

The adaptive leaky integrate-and-fire (ALIF) model is fundamental within computational neuroscience and has been instrumental in studying our brains 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× 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.

Authors: Taylor, L; King, AJ; Harper, NS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Curran Associates
• Host title: Advances in Neural Information Processing Systems 36 (NeurIPS 2023)
• Volume: 36
• Pages: 59360-59374
• Publication date: 2024-07-01
• Acceptance date: 2023-09-21
• Event title: 37th Conference on Neural Information Processing Systems (NeurIPS 2023)
• Event location: New Orleans, LA, USA.
• Event website: https://neurips.cc/Conferences/2023
• Event start date: 2023-12-10
• Event end date: 2023-12-16
• ISSN: 1049-5258
• ISBN: 9781713899921
• Language: English