Accelerating Brain Simulations with the Fast Multipole Method

TL;DR

This paper introduces a fast approximation method based on the fast multipole method (FMM) to significantly accelerate brain simulations involving structural plasticity, improving scalability and computational efficiency.

Contribution

It adapts the fast multipole method for brain simulation models, achieving linear time complexity and outperforming previous Barnes-Hut based approximations.

Findings

Achieves linear time complexity in brain simulation calculations.

Faster practical performance compared to Barnes-Hut approximation.

Enables scalable simulation of neural plasticity processes.

Abstract

The brain is probably the most complex organ in the human body. To understand processes such as learning or healing after brain lesions, we need suitable tools for brain simulations. The Model of Structural Plasticity offers a solution to that problem. It provides a way to model the brain bottom-up by specifying the behavior of the neurons and using structural plasticity to form the synapses. However, its original formulation involves a pairwise evaluation of attraction kernels, which drastically limits scalability. While this complexity has recently been decreased to $O(n\cdot \log^2 n)$ after reformulating the task as a variant of an n-body problem and solving it using an adapted version of the Barnes-Hut approximation, we propose an even faster approximation based on the fast multipole method (FMM). The fast multipole method was initially introduced to solve pairwise interactions in linear time. Our adaptation achieves this time complexity, and it is also faster in practice than the previous approximation.