Reverse Predictivity at CIAN Postdoctoral Seminar
Date:
Systems neuroscience increasingly relies on large-scale computational models to understand how neurons give rise to behavior. How good are these models? An intuitive benchmark comes from asking: how well can one primate brain predict the activity of another? A model that is fully brain-aligned should show a similar symmetry with the brain. In practice, however, model–brain comparisons have only followed a one-way approach. Artificial neural networks (ANNs) are typically evaluated by how well their features predict neural responses (forward predictivity), and not whether the ANN’s internal responses are equally predictable from primate brain activity. Addressing this gap, we introduced reverse predictivity (Muzellec et al., 2025), quantifying how well neural population activity predicts individual ANN units. Applying this bidirectional framework to macaque inferior temporal cortex we revealed representational mismatches that forward metrics alone fail to detect. Interestingly, factors improving forward predictivity (e.g., increasing model capacity, optimizing single-task performance) reduced reverse predictivity. To avoid such a bottleneck in current model design, we further demonstrated (Ziaee et al., 2025) that ANNs trained with multiple, behaviorally meaningful objectives can achieve improvements in both directions of predictability. Together, these results motivate bidirectional evaluation as a general principle for assessing and developing brain-like ANN models. In this seminar, I will argue that ANNs remain our strongest mechanistic hypotheses of brain function and discuss how reverse predictivity can guide the design of more biologically grounded models.