Applicability of scaling laws to vision encoding models
This work addresses the challenge of building accurate visual models of the brain for neuroscience, but it is incremental as it applies known scaling laws to a specific dataset.
The paper tackled the problem of predicting brain activity from images using vision encoding models, finding that both increasing fMRI training sample size and vision model parameter size improve prediction accuracy according to scaling laws, with models ranging from 86M to 4.3B parameters.
In this paper, we investigated how to build a high-performance vision encoding model to predict brain activity as part of our participation in the Algonauts Project 2023 Challenge. The challenge provided brain activity recorded by functional MRI (fMRI) while participants viewed images. Several vision models with parameter sizes ranging from 86M to 4.3B were used to build predictive models. To build highly accurate models, we focused our analysis on two main aspects: (1) How does the sample size of the fMRI training set change the prediction accuracy? (2) How does the prediction accuracy across the visual cortex vary with the parameter size of the vision models? The results show that as the sample size used during training increases, the prediction accuracy improves according to the scaling law. Similarly, we found that as the parameter size of the vision models increases, the prediction accuracy improves according to the scaling law. These results suggest that increasing the sample size of the fMRI training set and the parameter size of visual models may contribute to more accurate visual models of the brain and lead to a better understanding of visual neuroscience.