Examining the Three‐Dimensional Spatial Architecture of Mouse Amygdala Engram Ensembles
Bibliographic record
Abstract
Memories are stored in a sparse population of neurons active at the time of an event, an engram ensemble, and reactivation of the engram ensemble drives memory recall. Although the amygdala is essential for fear memory encoding and recall, the precise spatial organization of engram neurons within amygdala nuclei remains an outstanding question. We hypothesized that the geometric architecture of an engram ensemble reflects its underlying function, thereby revealing novel organizational principles of memory coding. Using tissue clearing and light-sheet imaging, we mapped Arc-expressing neurons across the entire mouse amygdala in 3D to examine the spatial architecture of engram ensembles for aversive (shock) and valence-neutral (no shock) contextual memories during encoding and recall. At the meso-scale level, we identified distinct spatial "hotspots" of engram neuron density during that were differentially engaged during fear conditioning and fear memory retrieval across multiple amygdala nuclei. Notably, we also found shared spatial features during the retrieval of aversive and non-aversive contextual memories. At the micro-scale level, unsupervised clustering analyses showed that aversive learning and recall was associated with increased clustering of active neurons in the lateral (LA) and basal (BA) nuclei, and selectively in the central capsular nucleus (CeC) during aversive encoding. Network graphs derived from the spatial organization of active neurons revealed highly clustered and assortative local graph structures across conditions. Assortativity increased in the CeC during aversive learning, and hub nodes increased in the LA during aversive learning and recall, and in the CeC during aversive learning. Together, these results suggest that both meso- and micro-scale spatial signatures of neuronal activity differ across amygdala subregions and vary with memory valence and stage. Such structure may shape information flow through amygdala circuits, enhancing signal-to-noise and improving the fidelity of memory encoding and retrieval.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".