The curious case of developmental BERTology: On sparsity, transfer learning, generalization and the brain

TL;DR

This paper investigates the parallels between deep learning models, especially large language models, and neuroscience, focusing on how biological neural development can inspire more efficient AI training and understanding brain maturation.

Contribution

It proposes a novel perspective linking neural development in biology with transfer learning and network compression in deep learning, offering insights into brain aging and maturation.

Findings

Biological neural development can inspire new AI optimization methods.

Transfer learning parallels brain maturation processes.

Network compression models brain aging effects.

Abstract

In this essay, we explore a point of intersection between deep learning and neuroscience, through the lens of large language models, transfer learning and network compression. Just like perceptual and cognitive neurophysiology has inspired effective deep neural network architectures which in turn make a useful model for understanding the brain, here we explore how biological neural development might inspire efficient and robust optimization procedures which in turn serve as a useful model for the maturation and aging of the brain.