Neural Functional Alignment Space: Brain-Referenced Representation of Artificial Neural Networks
This work addresses the challenge of comparing neural networks in a biologically meaningful way for researchers in neuroscience and AI, though it appears incremental as it builds on existing alignment and dynamical methods.
The authors tackled the problem of characterizing artificial neural networks by proposing the Neural Functional Alignment Space (NFAS), a brain-referenced framework that models representation dynamics across network depth, and they found structured organization across 45 pretrained models, including modality-specific clustering and cross-modal convergence.
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