Norepinephrine as a spatial memory reset signal
Bibliographic record
Abstract
Contextual information is represented in the hippocampus (HPC) partially through the recruitment of distinct neuronal ensembles. It is believed that reactivation of these ensembles underlies memory retrieval processes. Recently, we showed that norepinephrine input from phasic locus coeruleus activation induces hippocampal plasticity resulting in the recruitment of new neurons and disengagement from previously established representations. We hypothesize that norepinephrine may provide a neuromodulatory mnemonic switch signaling the HPC to move from a state of retrieval to encoding in the presence of novelty, and therefore, plays a role in memory updating. Here, we tested whether bilateral dorsal dentate gyrus (dDG) infusions of the β-adrenergic receptor (BAR) agonist isoproterenol (ISO), administered prior to encoding or retrieval, would impair spatial working and reference memory by reverting, the system to encoding (thereby recruiting new neurons) potentially interfering with the retrieval of the previously established spatial ensemble. We also investigated whether dDG infusions of ISO could promote cognitive flexibility by switching the system to encoding when it is adaptive (ie, when new information is presented, eg, reversal learning). We found that intra-dDG infusions of ISO given prior to retrieval caused deficits in working and reference memory which was blocked by pretreatment with the BAR-antagonist, propranolol (PRO). In contrast, ISO administered prior to reversal learning led to improved performance. These data support our hypothesis that norepinephrine serves as a novelty signal to update HPC contextual representations via BAR activation-facilitated recruitment of new neurons. This can be both maladaptive and adaptive depending on the situation.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".