Scaling Parameter-Efficient Fine-Tuning for Brain Encoding

A research paper on brain encoding models argues that scaling parameter-efficient fine-tuning (PEFT) is a viable alternative to fitting readouts from frozen pretrained backbones, a common approach that struggles because neural data is costly and limited. The work addresses the difficulty of training large models from scratch or fully fine-tuning them on neural data after task-pretraining. The paper's specific results, figures and authors are not detailed in the available text.

Brain encoding models aim to predict neural responses to stimuli. The most widely used approach fits readouts from frozen pretrained backbones. Using neural data to train large models from scratch or fully fine-tune them after task-pretraining has proven difficult because neural data is costly and l