Scale-Invariance Drives Convergence in AI and Brain Representations

• URL: http://arxiv.org/abs/2506.12117v1
• Date: Fri, 13 Jun 2025 15:36:04 GMT
• Title: Scale-Invariance Drives Convergence in AI and Brain Representations
• Authors: Junjie Yu, Wenxiao Ma, Jianyu Zhang, Haotian Deng, Zihan Deng, Yi Guo, Quanying Liu,
• Abstract summary: Recent studies indicate that large-scale AI models often converge toward similar internal representations that also align with neural activity.<n>We quantify two core aspects of scale-invariance in AI representations: dimensional stability and structural similarity across scales.<n>Our analysis reveals that embeddings with more consistent dimension and higher structural similarity across scales align better with fMRI data.
• Score: 7.318297580732467
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite variations in architecture and pretraining strategies, recent studies indicate that large-scale AI models often converge toward similar internal representations that also align with neural activity. We propose that scale-invariance, a fundamental structural principle in natural systems, is a key driver of this convergence. In this work, we propose a multi-scale analytical framework to quantify two core aspects of scale-invariance in AI representations: dimensional stability and structural similarity across scales. We further investigate whether these properties can predict alignment performance with functional Magnetic Resonance Imaging (fMRI) responses in the visual cortex. Our analysis reveals that embeddings with more consistent dimension and higher structural similarity across scales align better with fMRI data. Furthermore, we find that the manifold structure of fMRI data is more concentrated, with most features dissipating at smaller scales. Embeddings with similar scale patterns align more closely with fMRI data. We also show that larger pretraining datasets and the inclusion of language modalities enhance the scale-invariance properties of embeddings, further improving neural alignment. Our findings indicate that scale-invariance is a fundamental structural principle that bridges artificial and biological representations, providing a new framework for evaluating the structural quality of human-like AI systems.

Related papers

• Deep Neural Networks as Iterated Function Systems and a Generalization Bound [2.7920304852537536]
  We show that two important deep architectures can be viewed as, or canonically associated with, place-dependent IFS.<n>We derive a Wasserstein bound for generative modeling that controls the collage-type approximation error between the data distribution and its image.
  arXiv  Detail & Related papers  (2026-01-27T07:32:49Z)
• Integrated representational signatures strengthen specificity in brains and models [8.045700364123645]
  Similarity Network Fusion (SNF) is a framework originally developed for multi-omics data integration.<n>SNF produces substantially sharper regional and model family-level separation than any single metric.<n>Clustering cortical regions using SNF-derived similarity scores reveals a clearer hierarchical organization.
  arXiv  Detail & Related papers  (2025-10-21T04:37:27Z)
• Adapting HFMCA to Graph Data: Self-Supervised Learning for Generalizable fMRI Representations [57.054499278843856]
  Functional magnetic resonance imaging (fMRI) analysis faces significant challenges due to limited dataset sizes and domain variability between studies.<n>Traditional self-supervised learning methods inspired by computer vision often rely on positive and negative sample pairs.<n>We propose adapting a recently developed Hierarchical Functional Maximal Correlation Algorithm (HFMCA) to graph-structured fMRI data.
  arXiv  Detail & Related papers  (2025-10-05T12:35:01Z)
• Structural Connectome Harmonization Using Deep Learning: The Strength of Graph Neural Networks [0.9663199711697325]
  Small sample sizes in structural connectome (SC) studies limit the development of reliable biomarkers for neurological and psychiatric disorders.<n>Large-scale multi-site studies have exist, but they have acquisition-related biases due to scanner heterogeneity.<n>We propose a site-conditioned deep harmonization framework that harmonizes SCs across diverse acquisition sites without requiring metadata.
  arXiv  Detail & Related papers  (2025-07-18T14:58:05Z)
• BrainSymphony: A Transformer-Driven Fusion of fMRI Time Series and Structural Connectivity [2.3486335708866606]
  BrainSymphony is a lightweight, parameter-efficient foundation model for neuroimaging.<n>It achieves state-of-the-art performance while being pre-trained on significantly smaller public datasets.<n>BrainSymphony establishes that architecturally-aware, multimodal models can surpass their larger counterparts.
  arXiv  Detail & Related papers  (2025-06-23T06:00:21Z)
• High-Fidelity Scientific Simulation Surrogates via Adaptive Implicit Neural Representations [51.90920900332569]
  Implicit neural representations (INRs) offer a compact and continuous framework for modeling spatially structured data.<n>Recent approaches address this by introducing additional features along rigid geometric structures.<n>We propose a simple yet effective alternative: Feature-Adaptive INR (FA-INR)
  arXiv  Detail & Related papers  (2025-06-07T16:45:17Z)
• Evaluating Representational Similarity Measures from the Lens of Functional Correspondence [1.7811840395202345]
  Neuroscience and artificial intelligence (AI) both face the challenge of interpreting high-dimensional neural data. Despite the widespread use of representational comparisons, a critical question remains: which metrics are most suitable for these comparisons?
  arXiv  Detail & Related papers  (2024-11-21T23:53:58Z)
• Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks [17.063133885403154]
  We integrate functional magnetic resonance imaging, diffusion tensor imaging, and structural MRI into a cohesive framework.<n>Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data.<n>The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth.
  arXiv  Detail & Related papers  (2024-08-26T13:16:42Z)
• Understanding the differences in Foundation Models: Attention, State Space Models, and Recurrent Neural Networks [50.29356570858905]
  We introduce the Dynamical Systems Framework (DSF), which allows a principled investigation of all these architectures in a common representation.<n>We provide principled comparisons between softmax attention and other model classes, discussing the theoretical conditions under which softmax attention can be approximated.<n>This shows the DSF's potential to guide the systematic development of future more efficient and scalable foundation models.
  arXiv  Detail & Related papers  (2024-05-24T17:19:57Z)
• Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
  We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues. PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target. Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
  arXiv  Detail & Related papers  (2023-09-27T14:39:41Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Similarity of Neural Architectures using Adversarial Attack Transferability [47.66096554602005]
  We design a quantitative and scalable similarity measure between neural architectures. We conduct a large-scale analysis on 69 state-of-the-art ImageNet classifiers. Our results provide insights into why developing diverse neural architectures with distinct components is necessary.
  arXiv  Detail & Related papers  (2022-10-20T16:56:47Z)
• Post-mortem on a deep learning contest: a Simpson's paradox and the complementary roles of scale metrics versus shape metrics [61.49826776409194]
  We analyze a corpus of models made publicly-available for a contest to predict the generalization accuracy of neural network (NN) models. We identify what amounts to a Simpson's paradox: where "scale" metrics perform well overall but perform poorly on sub partitions of the data. We present two novel shape metrics, one data-independent, and the other data-dependent, which can predict trends in the test accuracy of a series of NNs.
  arXiv  Detail & Related papers  (2021-06-01T19:19:49Z)
• Deep Learning Identifies Neuroimaging Signatures of Alzheimer's Disease Using Structural and Synthesized Functional MRI Data [8.388888908045406]
  We propose a potential solution by first learning a structural-to-functional transformation in brain MRI. We then synthesize spatially matched functional images from large-scale structural scans. We identify the temporal lobe to be the most predictive structural-region and the parieto-occipital lobe to be the most predictive functional-region of our model.
  arXiv  Detail & Related papers  (2021-04-10T03:16:33Z)
• Deep Representational Similarity Learning for analyzing neural signatures in task-based fMRI dataset [81.02949933048332]
  This paper develops Deep Representational Similarity Learning (DRSL), a deep extension of Representational Similarity Analysis (RSA) DRSL is appropriate for analyzing similarities between various cognitive tasks in fMRI datasets with a large number of subjects.
  arXiv  Detail & Related papers  (2020-09-28T18:30:14Z)
• Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis [5.23143327587266]
  We review a few recently developed algorithms in various domains of fMRI research. These algorithms all tackle the challenges in fMRI similarly. We advocate wider adoption of explicit model construction in cognitive neuroscience.
  arXiv  Detail & Related papers  (2020-05-11T06:32:54Z)

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.