We show that place cell representational drift and remapping are not random at the population level and supported by dedicated circuit mechanisms. This enabled the generalization of task variables (task structure, choice, time) across environments.

We found that the hippocampus supports rapid generalization in mice by generating disentangled memory representations, where different aspects of experience are encoded independently. These low-dimensional population dynamics evolved with learning.

Intelligent behavior involves generalizing from experience and applying prior knowledge to new situations. Traditional models attribute this to gradual learning in the cortex. However, such a slow process can't account for animals’ rapid generalization from limited experience

Excited to share our latest story! We found disentangled memory representations in the hippocampus that generalized across time and environments, despite the seemingly random drift and remapping of single cells. This code enabled the transfer of prior knowledge to solve new tasks

We found that the hippocampus supports rapid generalization in mice by generating disentangled memory representations, where different aspects of experience are encoded independently. These low-dimensional population dynamics evolved with learning.

Intelligent behavior involves generalizing from experience and applying prior knowledge to new situations. Traditional models attribute this to gradual learning in the cortex. However, such a slow process can't account for animals’ rapid generalization from limited experience