Neural Functional Alignment Space: Brain-Referenced Representation of Artificial Neural Networks

2026-02-28

AI Summary

This work proposes the Neural Functional Alignment Space (NFAS) to address the challenge of uniformly evaluating representations across diverse neural architectures, which existing brain-referenced approaches struggle with due to their reliance on static layers or task-specific alignment. NFAS leverages Dynamic Mode Decomposition (DMD) to extract the evolutionary trajectory of stimulus representations along network depth and projects these trajectories into a biologically anchored coordinate system constructed from multimodal neural responses. This enables a unified, cross-modal, and cross-architectural characterization of model functionality. The authors introduce the Signal-Noise Consistency Index (SNCI) to quantify alignment quality. Experiments across 45 pretrained models reveal that representations in NFAS cluster by modality and exhibit cross-modal convergence within integrative cortical systems, demonstrating that representational dynamics provide a general foundation for functional model evaluation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Multi-modal Vision

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

We propose the Neural Functional Alignment Space (NFAS), a brain-referenced representational framework for characterizing artificial neural networks on equal functional grounds. NFAS departs from conventional alignment approaches that rely on layer-wise features or task-specific activations by modeling the intrinsic dynamical evolution of stimulus representations across network depth. Specifically, we model layer-wise embeddings as a depth-wise dynamical trajectory and apply Dynamic Mode Decomposition (DMD) to extract the stable mode. This representation is then projected into a biologically anchored coordinate system defined by distributed neural responses. We also introduce the Signal-to-Noise Consistency Index (SNCI) to quantify cross-model consistency at the modality level. Across 45 pretrained models spanning vision, audio, and language, NFAS reveals structured organization within this brain-referenced space, including modality-specific clustering and cross-modal convergence in integrative cortical systems. Our findings suggest that representation dynamics provide a principled basis for

Problem

• neural alignment
• brain-referenced representation
• artificial neural networks
• cross-modal comparison
• representational dynamics

Innovation

• Neural Functional Alignment Space
• Dynamic Mode Decomposition
• brain-referenced representation
• representation dynamics
• Signal-to-Noise Consistency Index