A GPU-based computational framework that bridges neuron simulation and artificial intelligence

Yichen Zhang, Gan He, Lei Ma, Xiaofei Liu, J. J. Johannes Hjorth, Alexander Kozlov, Yutao He, Shenjian Zhang, Jeanette Hellgren Kotaleski, Yonghong Tian, Sten Grillner, Kai Du () and Tiejun Huang
Additional contact information
Yichen Zhang: Peking University
Gan He: Peking University
Lei Ma: Peking University
Xiaofei Liu: Peking University
J. J. Johannes Hjorth: Royal Institute of Technology KTH
Alexander Kozlov: Royal Institute of Technology KTH
Yutao He: Peking University
Shenjian Zhang: Peking University
Jeanette Hellgren Kotaleski: Royal Institute of Technology KTH
Yonghong Tian: Peking University
Sten Grillner: Karolinska Institute
Kai Du: Peking University
Tiejun Huang: Peking University

Nature Communications, 2023, vol. 14, issue 1, 1-18

Abstract: Abstract Biophysically detailed multi-compartment models are powerful tools to explore computational principles of the brain and also serve as a theoretical framework to generate algorithms for artificial intelligence (AI) systems. However, the expensive computational cost severely limits the applications in both the neuroscience and AI fields. The major bottleneck during simulating detailed compartment models is the ability of a simulator to solve large systems of linear equations. Here, we present a novel Dendritic Hierarchical Scheduling (DHS) method to markedly accelerate such a process. We theoretically prove that the DHS implementation is computationally optimal and accurate. This GPU-based method performs with 2-3 orders of magnitude higher speed than that of the classic serial Hines method in the conventional CPU platform. We build a DeepDendrite framework, which integrates the DHS method and the GPU computing engine of the NEURON simulator and demonstrate applications of DeepDendrite in neuroscience tasks. We investigate how spatial patterns of spine inputs affect neuronal excitability in a detailed human pyramidal neuron model with 25,000 spines. Furthermore, we provide a brief discussion on the potential of DeepDendrite for AI, specifically highlighting its ability to enable the efficient training of biophysically detailed models in typical image classification tasks.

Date: 2023
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DOI: 10.1038/s41467-023-41553-7

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