Asset Details
Do you wish to reserve the book?
Abstract representations emerge in human hippocampal neurons during inference
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Abstract representations emerge in human hippocampal neurons during inference
Do you wish to request the book?
Abstract representations emerge in human hippocampal neurons during inference
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Abstract representations emerge in human hippocampal neurons during inference
2024
Overview
Humans have the remarkable cognitive capacity to rapidly adapt to changing environments. Central to this capacity is the ability to form high-level, abstract representations that take advantage of regularities in the world to support generalization
1
. However, little is known about how these representations are encoded in populations of neurons, how they emerge through learning and how they relate to behaviour
2
,
3
. Here we characterized the representational geometry of populations of neurons (single units) recorded in the hippocampus, amygdala, medial frontal cortex and ventral temporal cortex of neurosurgical patients performing an inferential reasoning task. We found that only the neural representations formed in the hippocampus simultaneously encode several task variables in an abstract, or disentangled, format. This representational geometry is uniquely observed after patients learn to perform inference, and consists of disentangled directly observable and discovered latent task variables. Learning to perform inference by trial and error or through verbal instructions led to the formation of hippocampal representations with similar geometric properties. The observed relation between representational format and inference behaviour suggests that abstract and disentangled representational geometries are important for complex cognition.
A task in which participants learned to perform inference led to the formation of hippocampal representations whose geometric properties reflected the latent structure of the task, indicating that abstract or disentangled neural representations are important for complex cognition.
