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

TL;DR

This paper introduces an algorithmic reinterpretation of the ALIF neuron model that reduces simulation complexity, enabling faster, more accurate neural simulations on GPUs, significantly improving training speed and precision in modeling cortical neurons.

Contribution

It presents a novel reinterpretation of the ALIF model that allows efficient parallel GPU simulation, overcoming the traditional speed-accuracy trade-off in neural modeling.

Findings

Over 50x training speedup on synthetic benchmarks

Comparable performance to standard ALIF on classification tasks

Accurate fitting of real cortical neuron recordings with fine temporal resolution

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.