Bibliographic record
Abstract
In 1904, Richard Semon introduced the term "engram" to describe the neural substrate responsible for (or at least important in) storing and recalling memories (i.e., a memory trace). The recent introduction of a vast array of powerful new tools to probe and manipulate memory function at the cell and neuronal circuit level has spurred an explosion of interest in studying the engram. However, the present "engram renaissance" was not borne in isolation but rather builds on a long tradition of memory research. We believe it is important to acknowledge the debts our current generation of scientists owes to those scientists who have offered key ideas, persevered through failed experiments and made important discoveries before us. Examining the past can also offer a fresh perspective on the present state and future promise of the field. Given the large amount of empirical advances made in recent years, it seems particularly timely to look back and review the scientists who introduced the seminal terminology, concepts, methodological approaches, and initial data pertaining to engrams. Rather than simply list their many accomplishments, here we color in some details of the lives and milestone contributions of our seven personal heroes of the engram (Richard Semon, Karl Lashley, Donald Hebb, Wilder Penfield, Brenda Milner, James McConnell, and Richard Thompson). In reviewing their historic role, we also illustrate how their work remains relevant to today's studies.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".