It appears you don't have support to open PDFs in this web browser. To view this file, Open with your PDF reader
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.
High computational cost severely limit the applications of biophysically detailed multi-compartment models. Here, the authors present DeepDendrite, a GPU-optimized tool that drastically accelerates detailed neuron simulations for neuroscience and AI, enabling exploration of intricate neuronal processes and dendritic learning mechanisms in these fields.
You have requested "on-the-fly" machine translation of selected content from our databases. This functionality is provided solely for your convenience and is in no way intended to replace human translation. Show full disclaimer
Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer
Details








1 Peking University, National Key Laboratory for Multimedia Information Processing, School of Computer Science, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319)
2 Peking University, National Key Laboratory for Multimedia Information Processing, School of Computer Science, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319); Beijing Academy of Artificial Intelligence (BAAI), Beijing, China (GRID:grid.511045.4)
3 Peking University, National Key Laboratory for Multimedia Information Processing, School of Computer Science, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319); Yunnan University, School of Information Science and Engineering, Kunming, China (GRID:grid.440773.3) (ISNI:0000 0000 9342 2456)
4 Royal Institute of Technology KTH, Science for Life Laboratory, School of Electrical Engineering and Computer Science, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746)
5 Royal Institute of Technology KTH, Science for Life Laboratory, School of Electrical Engineering and Computer Science, Stockholm, Sweden (GRID:grid.5037.1) (ISNI:0000000121581746); Karolinska Institute, Department of Neuroscience, Stockholm, Sweden (GRID:grid.4714.6) (ISNI:0000 0004 1937 0626)
6 Peking University, National Key Laboratory for Multimedia Information Processing, School of Computer Science, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319); Peking University, School of Electrical and Computer Engineering, Shenzhen Graduate School, Shenzhen, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319)
7 Karolinska Institute, Department of Neuroscience, Stockholm, Sweden (GRID:grid.4714.6) (ISNI:0000 0004 1937 0626)
8 Peking University, Institute for Artificial Intelligence, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319)
9 Peking University, National Key Laboratory for Multimedia Information Processing, School of Computer Science, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319); Beijing Academy of Artificial Intelligence (BAAI), Beijing, China (GRID:grid.511045.4); Peking University, Institute for Artificial Intelligence, Beijing, China (GRID:grid.11135.37) (ISNI:0000 0001 2256 9319)