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.

翻译：神经科学与人工智能代表了通往通用智能的两条不同却互补的路径。然而，在人工智能研究和应用持续繁荣的当下，这两个领域之间的转化协同效应却日益难以捉摸——这主要受制于日益扩大的基础设施不兼容性：现代AI框架缺乏对生物物理真实性的原生支持，而神经模拟工具则难以适应基于梯度的优化和神经形态硬件部署。为弥合这一鸿沟，我们提出了BrainFuse，一个为生物物理神经模拟和基于梯度的学习提供全面支持的统一基础设施。通过应对算法、计算和部署方面的挑战，BrainFuse展现出三项核心能力：（1）将详细的神经元动力学算法性地集成到可微分学习框架中；（2）系统级优化，可在GPU上将可定制的离子通道动力学加速高达3000倍；（3）具有高度兼容性流水线的可扩展计算，用于神经形态硬件部署。我们通过AI和神经科学任务展示了这一全栈设计，涵盖从基础神经元模拟和功能柱建模到现实世界部署与应用场景。对于神经科学，BrainFuse支持多尺度生物建模，能够在单个神经形态芯片上部署约38,000个Hodgkin-Huxley神经元及1亿个突触，功耗低至1.98 W。对于AI，BrainFuse促进了真实生物神经元模型的协同应用，展示了由复杂HH动力学赋予的、对输入噪声增强的鲁棒性以及改进的时间处理能力。因此，BrainFuse作为一个基础引擎，有助于促进跨学科研究并加速下一代仿生智能系统的发展。