Title: Let Brain Rhythm Shape Machine Intelligence for Connecting Dots on Graphs
Venue: NeurIPS 2025
Accepted: yes
Keywords: Neuroimage

Brain dynamics

Kuramoto model

Graph Learning
Abstract: In both neuroscience and artificial intelligence (AI), it is well-established that neural “coupling” gives rise to dynamically distributed systems. These systems exhibit self-organized spatiotemporal patterns of synchronized neural oscillations, enabling the representation of abstract concepts. By capitalizing on the unprecedented amount of human neuroimaging data, we propose that advancing the theoretical understanding of rhythmic coordination in neural circuits can offer powerful design principles for the next generation of machine learning models with improved efficiency and robustness. To this end, we introduce a physics-informed deep learning framework for \underline{B}rain \underline{R}hythm \underline{I}dentification by \underline{K}uramoto and \underline{C}ontrol (coined \textit{BRICK}) to characterize the synchronization of neural oscillations that shapes the dynamics of evolving cognitive states.  Recognizing that brain networks are structurally connected yet behaviorally dynamic, we further conceptualize rhythmic neural activity as an artificial dynamical system of coupled oscillators, offering a shared mechanistic bridge to brain-inspired machine intelligence. By treating each node as an oscillator interacting with its neighbors, this approach moves beyond the conventional paradigm of graph heat diffusion and establishes a new regime of representation compression through oscillatory synchronization. Empirical evaluations demonstrate that this synchronization-driven mechanism not only mitigates over-smoothing in deep GNNs but also enhances the model’s capacity for reasoning and solving complex graph-based problems.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new learning framework called BRICK. It develops more effective machine learning algorithm design principles by simulating brain rhythm mechanisms, thereby connecting neuroscience and artificial intelligence. The goal of BRICK is to identify brain rhythms, and to develop an oscillator representation and control mechanism closely integrated with fundamental neuroscience principles. The representations output by this model highly reflect neuroscientific principles (i.e., brain rhythms). Furthermore, building upon this, the paper extends this concept to the graph domain, introducing a new graph neural network architecture called BIG-NOS. This model improves existing graph learning models and possesses the ability to avoid the over-smoothing issue.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper presents novel and significant work. It introduces a new perspective to the AI field, particularly in graph neural networks, by proposing a biologically inspired mechanism of brain rhythms for machine learning. \n2. This paper experimentally demonstrates the model's ability to identify brain rhythms. For example, BRICK can decode dynamic brain states based on neural synchronisation patterns and achieves significantly superior performance compared to existing GNN models on human brain datasets such as HCP-A, HCP-YA, and HCP-WM.\n3. This paper effectively avoids the over-smoothing issue in GNNs. The proposed BIG-NOS explicitly avoids over-smoothing by evolving graph features within a latent oscillatory phase space, rather than through traditional heat diffusion methods.\n\nWeaknesses:\n1. The paper does not compare with popular models like graph diffusion and graph transformer, thus lacking a comprehensive comparison with the latest state-of-the-art graph learning models [1-3].\n2. The interpretability (biological plausibility) analysis is insufficient. In brain rhythm identification, the authors demonstrate visualisation results of brain region synchronisation, but do not provide further analysis on whether the output representations are consistent with biological characteristics of the brain. For instance, Line 305 states that the proposed mode uncovers functional communities that are not\nonly align with known subnetworks but also capture novel patterns in brain organisation, but a more detailed biological interpretation is lacking.\n3. The paper lacks theoretical proof for its core mechanism (e.g., how oscillatory synchronisation mitigates over-smoothing). Although an energy function $E$ is proposed, it does not provide complete evidence to support its theoretical results. Although $E$ is claimed to be a \"natural\" Lyapunov function, no detailed mathematical derivations or proofs are given.\n\n[1] Yang, H., Wang, B., & Jia, J. (2024). Gnncert: Deterministic certification of graph neural networks against adversarial perturbations. In The Twelfth International Conference on Learning Representations.\n\n[2] Chamberlain, B., Rowbottom, J., Gorinova, M. I., Bronstein, M., Webb, S., & Rossi, E. (2021, July). Grand: Graph neural diffusion. In International conference on machine learning (pp. 1407-1418). PMLR.\n\n[3] Yun, S., Jeong, M., Kim, R., Kang, J., & Kim, H. J. (2019). Graph transformer networks. Advances in neural information processing systems, 32.\n\nQUESTIONS:\nSee weaknesses. Overall, I believe this paper is a good work. I’m happy to raise my score if the authors address my concerns well.\n\nLIMITATIONS:\nSee weaknesses.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis framework presents a physics and neuroscience-informed deep learning framework. It integrates with a synchronization mechanism of neural oscillations inside a graph representation learning framework. Motivated by addressing the oversmoothing issue of conventional GNNs, it proposed to utilize the brain rhythms in the artificial dynamical system. This method integrates a Kuramoto model with attending memory for modeling oscillatory synchronization in brain regions. It has been applied to solve two tasks on brain rhythm identification and conventional tasks on graph data, demonstrating superior performance compared to existing baselines.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. This work presents a novel solution to address the oversmoothing issue of conventional GNNs, it incorporated with a physics-informed structure with Kuramoto model with attending memory inside a graph representation framework. This system has novel designs in governing equation, learning mechanism, and attention mechanism compared to conventional GNN models.\n2. The model demonstrates superior performance on two tasks (brain rhythm identification and graph tasks) compared to existing baselines. \n3. This model with good interpretability, and demonstrate interesting visualizations of synchronization patterns in Fig 3.\n4. Multiple ablations studies are conducted.\n\n**Weaknesses**\n1. It would be more impactful to link the synchronization mechanisms to neuroscience domains, and explains why it might be more superior compared to conventional approachs in brain rhythm identification task, and how the discovery in Fig 3 might be relevant to existing studies.\n2. Missing comparisons of the computational cost of the proposed methods compared to existing methods.\n\nQUESTIONS:\n1. What might be challenges for the optimization solver for the proposed graph learning procedure, would this guarantee to converge, and how it is sensitive to initialization and hyperparameters?\n2. Would this be more computational expensive compared to conventional graph learning methods, and how it will be scale to larger graph?\n\nLIMITATIONS:\n1. Include more discussions with neuroscience literatures and mechanisms would be more impactful.\n2. Include more discussion and evaluations on the computational cost, and scalability.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper presents BRICK, a novel deep learning-based framework designed to decode cognitive states with strong biological inspiration. Unlike traditional artificial neural networks (such as the perceptron or multilayer perceptron), which drew only loose inspiration from neuroscience and abstracted away much of the brain’s complexity, BRICK integrates neuroscientific principles, such as fluctuating brain activity, neural oscillatory synchronization, and attentional memory to more closely emulate neurobiological processes.\t \n\nSpecifically, BRICK extends these neuroscience principles into the graph domain, employing a physics-informed model for brain rhythm identification. This model utilizes a Kuramoto model-inspired control mechanism to capture memory-guided neural synchronization. This approach enables the development of a neurobiologically inspired GNN, called BIG-NOS, which specifically addresses main limitations like over-smoothing while offering scalability across node and graph classification tasks.\n\nExtensive evaluation on publicly available neuroimaging and graph datasets demonstrate that BRICK effectively synchronizes brain regions, yielding synchronization patterns closely aligned with specific cognitive states. BIG-NOS achieves state-of-the-art (SOTA) performance on a range of GNN benchmarks.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths \n- Originality.\nThe main strength of this paper is to introduce a new design paradigm for developing GNNs through the lens of coupled neural oscillators. The authors highlight a link between the neuroscience and machine learning, where the last one is used learn the parameters the Kuramoto model, where a new term is included to incorporate the concept of the attending memory.\n\n- Empirical validation. \nThe claim related to BRICK ant to BIG-NOS are well-supported by the experimental results, that show:\n   - BRICK ability to synchronize brain region. Especially even the experiments in the absence of ground truth, show the BRICK’s capability of identifying clusters of functional subnetworks.\n  - BIG-NOSE achieve SOTA performance on node and graph classification showing robustness to over-smoothing and scalability issues.\n\n\nWeaknesses:\n\nClarity. The main weakness is related to the clarity of the paper. Specifically, the two following points limits the readability of the paper.\n- Related work.\n     - It is not clear what are the differences between the authors' modified Kumamoto model in BRICK and the ones proposed in [9,10]. \n    -  It is not clear what are the differences between the Kumamoto model in BRICK and the work [44]. In that regard, the authors contribution is to put the spotlight on bridging the reciprocal relationship between neuroscience and AI through the lens of governing equations in the dynamical system.\n\n- Methods. The readability will improve to incorporate more information about the Big-NOS network architecture.\n\n[9] Joana Cabral, Etienne Hugues, Olaf Sporns, and Gustavo Deco. Role of local network oscillaions in resting-state functional c\tonnectivity. Neuroimage, 57(1):130–139, 2011.\n\n[10] Katerina Capouskova, Morten L Kringelbach, and Gustavo Deco. Modes of cognition: Evidence\nfrom metastable brain dynamics. NeuroImage, 260:119489, 2022.\n\n[44] Takeru Miyato, Sindy Löwe, Andreas Geiger, and Max Welling. Artificial kuramoto oscillatory neurons. arXiv preprint arXiv:2410.13821, 2024.\n\nQUESTIONS:\n- What are the pros of BRICK model w.r.t. to related work [9,10] that used Kuramoto model to describe the coupling between brain structure and function? What are the advantages of BRICK compared to the work in [44]?\n\n- Can you clarify why equation 4 can be interpreted as gradient flow? I guess that in line 185 the authors want to provide intuition behind eq. 5 not 4.\n\n- What kind on nerural nertwork architecture  did the author use to o parameterize the natural frequency Ω (lines 201-202)?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nInspired by neural oscillation mechanism, the authors propose novel deep learning methods that utilize neural oscillation, BRICK and BIG-NOS. BRICK was applied for brain fMRI datasets. Specifically, BRICK achieved the best performance in brain analysis tasks, compared to simple GNN baselines. BIG-NOS was applied for graph benchmark datasets, and the authors report its superior node and graph classification accuracies compared to simple GNN baselines. The authors argue that the proposed approaches represent a new graph learning mechanism with SOTA performance.\n\nSTRENGTHS AND WEAKNESSES:\nThe **key strengths** include:\n\n- [S1 novelty]. The proposed approaches seem somewhat novel.\n- [S2 visualization]. The visualizations are among the most beautiful ones I have seen in AI conferences.\n\nHowever, I note two **critical limitations** to the present work.\n\n- [W1 poor experimental setting]. **Many of the reported baseline performances in Table 3 are substantially lower than those reported in eariler studies**. For instance, GCNII performance on Cora is reported as 79.92, whereas the other works generally report up to 86 in the same train/val/test split. In fact, I have run many experiments with the baselines before, and the reported performances are definitely not optimal. I assume this is due to poor choice of hyperparameters. According to Appendix A.2, the authors did not tune some of the key hyperparameters for all baseline methods, and some other key hyperparameters, such as learning rate, are not reported. With such poor experimental setting, I find it hard to trust the experimental outcomes.\n- [W2 lack of method justification]. I am not convinced of the justification to join neural oscillation with graph learning. Due to the issue in W1, empirical justification is hardly achieved. No formal theories were developed to justify them. The motivation illustrated in the introduction section (e.g., node in a graph as a couple oscillator) is only speculative and have not been analyzed or supporte by the prior works. Thus, I am not convinced why the proposed method is necessary or important.\n\nBesides, I also find some **notable limitations**.\n\n- [W3 missing details]. Some of important details are missing. For instance, formal description of the proposed method BIG-NOS seems to be missing. Is the Governing Equation in Table 1 the exact functional form of BIG-NOS? If so, please clarify. Also, the Figure 3 results should be buttressed with numeric outcomes. With only the plots, it is hard to objectively discern how they support the authors’ claims.\n- [W4 missing prior works]. There have been some previous works that connect neural oscillation with graph neural networks [1,2]. However, the authors cited none of them. The authors should clarify how the proposed method compare to those earlier works.\n    - [1] Graph-Coupled Oscillator Networks, ICML 2022\n    - [2] From Coupled Oscillators to Graph Neural Networks: Reducing Over-smoothing via a Kuramoto Model-based Approach, ICML 2024\n- [W5 unsupported claims]. The authors claim for SOTA performance. However, all the baselines are outdated, with the most recent one published in 2021. Even if the issue raised in W1 is somewhat justified, there is no evidence to demonstrate that the proposed method is not SOTA.\n\nQUESTIONS:\nSee weaknesses\n\nLIMITATIONS:\nYes, but the authors only discussed marginal limitations.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-01"}