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

• URL: http://arxiv.org/abs/2311.11390v1
• Date: Sun, 19 Nov 2023 18:21:45 GMT
• Title: Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons
• Authors: Luke Taylor, Andrew J King, Nicol S Harper
• Abstract summary: We present an algorithmically reinterpreted the adaptive integrate-and-fire (ALIF) model. We obtain over a $50times$ training speedup using small DTs on synthetic benchmarks. We also showcase how our model makes it possible to quickly and accurately fit real electrophysiological recordings of cortical neurons.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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