Microcosms of the brain what sensorimotor systems reveal about the mind
Bibliographic record
Abstract
Abstract How can we understand a system as intricate as the human brain? Microcosms of the Brain presents a bold new approach to understanding this incredibly complex organ. It argues that the key to understanding brain function lies in the sensorimotor systems-those that gather sensory data such as light and sound, and use them to control action-steering the eyes, head, or limbs. The book shows how these subsystems can provide a microcosm of the brain-small enough to be analysed, but substantial enough to reveal general principles of brain function. By studying these simple subsystems and simulating their behaviour computationally, we can get some answers to the bigger questions about brain function. In ten chapters Tweed explores ten concepts that may help form a basis for the computerized neuroscience of the future: optimization, computation, complexity, learning, dynamics, interfaces, loops, degrees of freedom, information, and inference. He explains these concepts in simple, non-mathematical language, and shows how they can bring some order to our view of the human brain. Written to be accessible to students and researchers in the cognitive sciences, this is a book that could dramatically change the way that we explore the human mind.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".