Back

Refinement and Retrospective Shift of the CA1 Place Code during Food-Carrying Decisions

Rezaei, Z.; Whishaw, I. Q.; Sutherland, R. J.; Chang, H.; Mohajerani, M.

2026-09-21 neuroscience
10.64898/2026.09.14.751532 bioRxiv
Show abstract

Food carrying is an adaptive component of foraging that integrates resource evaluation with home-directed navigation, providing a naturalistic model for investigating how the brain organizes goal-directed behaviour. Despite extensive work on hippocampal coding during navigation and homing, how CA1 representations are reorganized when animals spontaneously transport acquired food home remains largely unexplored. We recorded dorsal CA1 population activity using one-photon calcium imaging with a V4 Miniscope while mice performed a self-paced foraging task in which food could be consumed at the acquisition site followed by a homeward return or carried to the home for consumption. Mice preferentially carried larger pellets, indicating that transport behaviour was sensitive to resource value. CA1 spatial coding differed systematically across behavioural outcomes. During carry-homeward runs, spatial representations were sparser, conveyed more spatial information, and showed greater trial-to-trial stability than during eat-inward runs, indicating a more precise and reliable place code during home-directed transport. Place fields also shifted forward during carrying, consistent with a retrospective bias toward recently traversed locations. By linking a self-generated food-handling decision to coordinated changes in the precision, stability, and temporal organization of CA1 activity, these findings extend hippocampal spatial-coding frameworks from trained navigation to ecologically grounded foraging behaviour.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.