ArticleNature communications2023
A GPU-based computational framework that bridges neuron simulation and artificial intelligence.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Lightweight Monocular Depth Estimation with Local Feature Enhancement Modules and Guided Data Augmentation.Sensors (Basel, Switzerland) · 2026Article
- Going deeper with morphologically detailed neural networks by simulation-based gradient propagation.Frontiers in computational neuroscience · 2026Article
- How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells.Frontiers in computational neuroscience · 2026Article
- Container-based framework for large-scale spiking network simulation.Frontiers in neuroscience · 2026Article
- Article
- Spiking world model with multicompartment neurons for model-based reinforcement learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.Nature methods · 2025Article
- Neuroengineering Frontiers: A Selective Review of Neural Interfaces, Brain-Machine Interactions, and Artificial Intelligence in Neurodegenerative Diseases.Applied sciences (Basel, Switzerland) · 2025Article
- Article
- Theoretical analysis of low-power deep synergistic sono-optogenetic excitation of neurons by co-expressing light-sensitive and mechano-sensitive ion-channels.Communications biology · 2025Article
- An integrative data-driven model simulating C. elegans brain, body and environment interactions.Nature computational science · 2024Article
- Deep learning applications in breast cancer histopathological imaging: diagnosis, treatment, and prognosis.Breast cancer research : BCR · 2024Review
- Real-time multicompartment Hodgkin-Huxley neuron emulation on SoC FPGA.Frontiers in neuroscience · 2024Article
- Reproducibility of biophysical in silico neuron states and spikes from event-based partial histories.PLoS computational biology · 2023Article
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Authors and funding
13 authors.
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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.
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