BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology

• URL: http://arxiv.org/abs/2601.21407v1
• Date: Thu, 29 Jan 2026 08:44:01 GMT
• Title: BrainFuse: a unified infrastructure integrating realistic biological modeling and core AI methodology
• Authors: Baiyu Chen, Yujie Wu, Siyuan Xu, Peng Qu, Dehua Wu, Xu Chu, Haodong Bian, Shuo Zhang, Bo Xu, Youhui Zhang, Zhengyu Ma, Guoqi Li,
• Abstract summary: Neuroscience and artificial intelligence represent distinct yet complementary pathways to general intelligence.<n>BrainFuse is a unified infrastructure that provides comprehensive support for biophysical neural simulation and gradient-based learning.<n>BrainFuse serves as a foundational engine to facilitate cross-disciplinary research and accelerate the development of next-generation bio-inspired intelligent systems.
• Score: 34.597782096704115
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Energy-based Autoregressive Generation for Neural Population Dynamics [12.867288040044501]
  We introduce a novel Energy-based Autoregressive Generation framework that employs an energy-based transformer learning temporal dynamics in latent space.<n>We show that EAG achieves state-of-the-art generation quality with substantial computational efficiency improvements.<n>These results demonstrate the effectiveness of energy-based modeling for neural population dynamics with applications in neuroscience research and neural engineering.
  arXiv  Detail & Related papers  (2025-11-18T07:11:29Z)
• Artificial intelligence as a surrogate brain: Bridging neural dynamical models and data [9.300290334520481]
  Recent breakthroughs in artificial intelligence (AI) are reshaping the way we construct computational counterparts of the brain, giving rise to a new class of surrogate brains''<n>We introduce a unified framework of constructing an AI-based surrogate brain that integrates forward modeling, inverse problem solving, and model evaluation.<n>We highlight that the learned surrogate brain serves as a simulation platform for dynamical systems analysis, virtual perturbation, and model-guided neurostimulation.
  arXiv  Detail & Related papers  (2025-10-11T18:23:10Z)
• Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective [53.556348738917166]
  Recent advances in agentic AI have led to systems capable of autonomous task execution and language-based reasoning.<n>Human spatial intelligence, rooted in integrated multisensory perception, spatial memory, and cognitive maps, enables flexible, context-aware decision-making in unstructured environments.
  arXiv  Detail & Related papers  (2025-09-11T05:23:22Z)
• Neuromorphic Computing with Multi-Frequency Oscillations: A Bio-Inspired Approach to Artificial Intelligence [12.240763476270018]
  Despite remarkable capabilities, artificial neural networks exhibit limited flexible, generalizable intelligence.<n>This limitation stems from their fundamental divergence from biological cognition that overlooks both neural regions' functional specialization and the temporal dynamics critical for coordinating these specialized systems.<n>We propose a tripartite brain-inspired architecture comprising functionally specialized perceptual, auxiliary, and executive systems.
  arXiv  Detail & Related papers  (2025-08-04T08:40:33Z)
• NOBLE -- Neural Operator with Biologically-informed Latent Embeddings to Capture Experimental Variability in Biological Neuron Models [63.592664795493725]
  NOBLE is a neural operator framework that learns a mapping from a continuous frequency-modulated embedding of interpretable neuron features to the somatic voltage response induced by current injection.<n>It predicts distributions of neural dynamics accounting for the intrinsic experimental variability.<n>NOBLE is the first scaled-up deep learning framework that validates its generalization with real experimental data.
  arXiv  Detail & Related papers  (2025-06-05T01:01:18Z)
• Neural Brain: A Neuroscience-inspired Framework for Embodied Agents [78.61382193420914]
  Current AI systems, such as large language models, remain disembodied, unable to physically engage with the world.<n>At the core of this challenge lies the concept of Neural Brain, a central intelligence system designed to drive embodied agents with human-like adaptability.<n>This paper introduces a unified framework for the Neural Brain of embodied agents, addressing two fundamental challenges.
  arXiv  Detail & Related papers  (2025-05-12T15:05:34Z)
• Augmenting learning in neuro-embodied systems through neurobiological first principles [42.810158068175646]
  We describe recent bioinspired models, learning rules, and architectures for augmenting artificial neural networks.<n>We propose a framework for augmenting ANNs, which has the potential to bridge the gap between neuroscience and AI.<n>We show how integrating biophysical principles into task-driven spiking neural networks and neuromorphic systems provides scalable solutions.
  arXiv  Detail & Related papers  (2024-07-05T14:11:28Z)
• A Differentiable Approach to Multi-scale Brain Modeling [3.5874544981360987]
  We present a multi-scale differentiable brain modeling workflow utilizing BrainPy, a unique differentiable brain simulator. At the single-neuron level, we implement differentiable neuron models and employ gradient methods to optimize their fit to electrophysiological data. On the network level, we incorporate connectomic data to construct biologically constrained network models.
  arXiv  Detail & Related papers  (2024-06-28T07:41:31Z)
• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi [77.34726150561087]
  We investigate the potential of Intel's fifth generation neuromorphic chip - Loihi' Loihi is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
  arXiv  Detail & Related papers  (2021-09-22T16:52:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.