BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866914289884856320 |
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| author | Chen, Baiyu Wu, Yujie Xu, Siyuan Qu, Peng Wu, Dehua Chu, Xu Bian, Haodong Zhang, Shuo Xu, Bo Zhang, Youhui Ma, Zhengyu Li, Guoqi |
| author_facet | Chen, Baiyu Wu, Yujie Xu, Siyuan Qu, Peng Wu, Dehua Chu, Xu Bian, Haodong Zhang, Shuo Xu, Bo Zhang, Youhui Ma, Zhengyu Li, Guoqi |
| contents | Neuroscience and artificial intelligence represent distinct yet complementary pathways to general intelligence. However, amid the ongoing boom in AI research and applications, the translational synergy between these two fields has grown increasingly elusive-hampered by a widening infrastructural incompatibility: modern AI frameworks lack native support for biophysical realism, while neural simulation tools are poorly suited for gradient-based optimization and neuromorphic hardware deployment. To bridge this gap, we introduce BrainFuse, a unified infrastructure that provides comprehensive support for biophysical neural simulation and gradient-based learning. By addressing algorithmic, computational, and deployment challenges, BrainFuse exhibits three core capabilities: (1) algorithmic integration of detailed neuronal dynamics into a differentiable learning framework; (2) system-level optimization that accelerates customizable ion-channel dynamics by up to 3,000x on GPUs; and (3) scalable computation with highly compatible pipelines for neuromorphic hardware deployment. We demonstrate this full-stack design through both AI and neuroscience tasks, from foundational neuron simulation and functional cylinder modeling to real-world deployment and application scenarios. For neuroscience, BrainFuse supports multiscale biological modeling, enabling the deployment of approximately 38,000 Hodgkin-Huxley neurons with 100 million synapses on a single neuromorphic chip while consuming as low as 1.98 W. For AI, BrainFuse facilitates the synergistic application of realistic biological neuron models, demonstrating enhanced robustness to input noise and improved temporal processing endowed by complex HH dynamics. BrainFuse therefore serves as a foundational engine to facilitate cross-disciplinary research and accelerate the development of next-generation bio-inspired intelligent systems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_21407 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology Chen, Baiyu Wu, Yujie Xu, Siyuan Qu, Peng Wu, Dehua Chu, Xu Bian, Haodong Zhang, Shuo Xu, Bo Zhang, Youhui Ma, Zhengyu Li, Guoqi Neural and Evolutionary Computing Neurons and Cognition Neuroscience and artificial intelligence represent distinct yet complementary pathways to general intelligence. However, amid the ongoing boom in AI research and applications, the translational synergy between these two fields has grown increasingly elusive-hampered by a widening infrastructural incompatibility: modern AI frameworks lack native support for biophysical realism, while neural simulation tools are poorly suited for gradient-based optimization and neuromorphic hardware deployment. To bridge this gap, we introduce BrainFuse, a unified infrastructure that provides comprehensive support for biophysical neural simulation and gradient-based learning. By addressing algorithmic, computational, and deployment challenges, BrainFuse exhibits three core capabilities: (1) algorithmic integration of detailed neuronal dynamics into a differentiable learning framework; (2) system-level optimization that accelerates customizable ion-channel dynamics by up to 3,000x on GPUs; and (3) scalable computation with highly compatible pipelines for neuromorphic hardware deployment. We demonstrate this full-stack design through both AI and neuroscience tasks, from foundational neuron simulation and functional cylinder modeling to real-world deployment and application scenarios. For neuroscience, BrainFuse supports multiscale biological modeling, enabling the deployment of approximately 38,000 Hodgkin-Huxley neurons with 100 million synapses on a single neuromorphic chip while consuming as low as 1.98 W. For AI, BrainFuse facilitates the synergistic application of realistic biological neuron models, demonstrating enhanced robustness to input noise and improved temporal processing endowed by complex HH dynamics. BrainFuse therefore serves as a foundational engine to facilitate cross-disciplinary research and accelerate the development of next-generation bio-inspired intelligent systems. |
| title | BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology |
| topic | Neural and Evolutionary Computing Neurons and Cognition |
| url | https://arxiv.org/abs/2601.21407 |