Predictive Coding of Reward in the Hippocampus
Bibliographic record
Abstract
Abstract A fundamental objective of the brain is to anticipate future outcomes 1–7 . This process requires learning the states of the world as well as the transitional relationships between those states. The hippocampal cognitive map is believed to be one such internal model 8 . However, evidence for predictive coding 9–12 and reward sensitivity 13–19 in the hippocampal neuronal representation suggests that its role extends beyond purely spatial representation. In fact, it raises the question of what kind of spatial representation is most useful for learning and maximizing future rewards? Here, we track the evolution of reward representation over weeks as mice learn to solve a cognitively demanding reward-based task. Our findings reveal a highly organized restructuring of hippocampal reward representations during the learning process. Specifically, we found multiple lines of evidence, both at the population and single-cell levels, that hippocampal representation becomes predictive of reward over weeks. Namely, both population-level information about reward and the percentage of reward-tuned neurons decrease over time. At the same time, the representation of the animals’ choice and reward approach period (the period between choice and reward) increased over time. By tracking individual reward cells across sessions, we found that neurons initially tuned for reward shifted their tuning towards choice and reward approach periods, indicating that reward cells backpropagate their tuning to anticipate reward with experience. These findings underscore the dynamic nature of hippocampal representations, highlighting their critical role in learning through the prediction of future outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".