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Researchers analyzed digital twin models of mouse visual cortex (V1) that predict neural activity from naturalistic videos, finding that models with similar prediction accuracy can differ substantially in their internal representations. By probing these models using orientation, contrast, and motion tests, examining individual unit tuning properties, and analyzing population-level geometry, they discovered that better neural prediction correlates with higher-dimensional representations and stronger feature decodability. However, models with comparable predictive performance can still show significant differences in how they encode visual information, suggesting that prediction accuracy alone is insufficient for evaluating these computational models.
Why it matters
This work establishes a framework for evaluating artificial neural network models of brain function beyond simple prediction metrics, which is critical as these models are increasingly used to design experiments and generate neuroscience hypotheses. The findings indicate that researchers must look deeper into how models represent information internally, not just how well they predict neural responses, to ensure digital twins accurately capture biological computations.
arXiv:2605.23122v2 Announce Type: replace
Abstract: Digital twins of sensory cortex serve as powerful response oracles. Although prediction accuracy is the central metric by which these models are evaluated, it provides limited insight into the latent representations that support those predictions. This becomes increasingly important as digital twins are used as in silico experimental systems for stimulus design and hypothesis generation: models with similar prediction accuracy may rely on different latent representations. We address this gap by systematically probing a family of digital twins of mouse V1 trained to predict neural activity from naturalistic videos recorded in freely moving mice. The models share the same training data and neural-prediction objective, but differ in visual-encoder architecture. For each frozen model, we characterize latent representations along three levels: (i) linear decodability from controlled visual probes of orientation, contrast, and motion; (ii) latent-unit tuning to canonical visual features including orientation selectivity, contrast response, spatial-frequency tuning; and (iii) population geometry of hidden-layer activity. Across architectures, better neural-response prediction correlates with stronger probe accuracy. Additionally, highly predictive models exhibit flatter hidden-population eigenspectra, indicating higher-dimensional representations closer to population-geometry signatures reported in mouse V1. Although these representational properties covary with prediction accuracy across architectures, digital twins with comparable prediction scores can still differ substantially in probe performance and latent-unit tuning. These results establish multi-level representational probing as a complement to standard neural-prediction evaluation, providing a framework for understanding digital twins not only as predictors, but also as substrates for studying visual computations.
Source: Beyond Neural Activity Prediction: Probing Latent Representations in Mouse V1 Digital Twins