“The Sleeping Beauty of the Brain”: Memory, MIT, Montreal, and the Origins of Neuroscience
Bibliographic record
Abstract
This essay traces the simultaneous development of two distinct efforts to unify the brain sciences in the twentieth century—one originating at the Montreal Neurological Institute (MNI) and the other at the Massachusetts Institute of Technology (MIT). Both efforts coalesced around investigations of memory but displayed profoundly different disciplinary styles. At the MNI, investigations of memory loss in surgical patients crystallized a form of brain research that eventually became the paradigm of interdisciplinarity for the International Brain Research Organization (IBRO). At MIT, meanwhile, the biophysicist Francis Schmitt aimed to transcend the different brain and mind sciences by discovering a “memory molecule” akin to DNA—this became the basis for his Neurosciences Research Program (NRP). While both organizations failed to achieve the unification they desired, IBRO and the NRP did achieve a social unification of the brain sciences in the 1960s. IBRO established much of the social capital for the Society for Neuroscience, and the NRP promoted the possibilities of a new transdisciplinary “neuroscience.” Beyond reframing the history of modern neuroscience, the story of the MNI/IBRO, the NRP, and the problem of memory can help us to examine different and contrasting approaches to “interdisciplinarity” in twentieth-century science.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".