Shijie Gu

The paper develops a task for rats to study self-correction and finds distinct hippocampal representations during this process.

The paper introduces a method to track neurons across multiple in-vivo imaging sessions without requiring full field-of-view registration, validated on simulated and experimental datasets.

This study shows that neural sequences in songbirds can form without tutor exposure but become tightly associated with new syllables after tutoring, with some birds failing to learn due to pre-existing crystallized sequences.

The paper introduces seqNMF, a software toolbox for extracting and analyzing temporal sequences in high-dimensional neural data, showing its effectiveness on simulated and real neural datasets.