<i>The Brain Abstracted: Simplification in the History and Philosophy of Neuroscience</i> , by M. Chirimuuta
Bibliographic record
Abstract
‘What’s in the brain that ink may character?’ asked Warren S. McCulloch (1964) (borrowing from Shakespeare) shortly before his death. Trained in philosophy, psychology, and medicine, McCulloch was one of the founding figures of cybernetics and neural network theory, and his spirit looms large over Chirimuuta’s landmark book, though she mentions him only a few times. McCulloch’s bardic question concerned the theoretical interrelations among real nervous systems, the postulated neurons of his neural nets (which were simplified, automata models of the nervous system), the kind of logical calculus needed to describe the activity of neural nets (which, in turn, exhibited their own complexity, particularly in the case of ‘nets with circles’, that is, with recurrent activity), and the mind. McCulloch’s question has hardly gone away, though its terms have changed. Today it concerns the relation between the physical brain in all its daunting complexity, neuroscience theories—particularly computational ones, which, though inevitably simplified compared to the brain, nevertheless exhibit their own complexity in the form of artificial neural networks—and the mind. What’s in the brain that neuroscience may characterize? How are computational models related to living brains? And how do these issues bear on neurophilosophy and philosophy of mind? These are the driving questions of The Brain Abstracted.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".