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.

探索如何构建高性能视觉编码模型以预测脑活动，通过研究功能性磁共振成像（fMRI）数据中的视觉模型参数大小和样本量对预测准确性的影响，结果表明增加训练集样本量和视觉模型参数大小能够提高预测准确性，从而更好地理解视觉神经科学。