Jiaqi Shang

This paper compares neural adaptation mechanisms to persistent activity in a visual change detection task using mice and artificial neural networks, finding that adaptation better matches neural and behavioral data.

The paper shows that cortical representations of visual stimuli align with in-class variability, enabling one-shot learning through noise-driven generalization.

The paper analyzes neuronal variability in the cortex and finds that its distribution resembles deep learning noise, suggesting it may aid generalization during learning.