Bibliographic record
Abstract
Networks of the Brain, by Olaf Sporns. The MIT Press, 2010, 375 pages (ISBN 978-0-262-01469-4, CA $40.00 Hardcover) Reviewed by MARK DALEY and JODY C. CULHAM DOI: 10.1037/00025503 The history of psychology and neuroscience is filled with a tension between theories emphasising localization of brain functions versus holistic processing. A third way has been emerging - viewing brain and mind in terms of dynamic, interacting networks. In his recent book Networks of the Brain, Olaf Sporns provides a much-welcomed synthesis of the network perspective. This perspective is not entirely new. First, as indicated by the well-selected quotes from eminent historical figures - Golgi, Cajal, Broca, James, and Hebb included - that Sporns employs to introduce each chapter, network ideas have been considered for some time. Second, the core tools used to analyse networks, primarily graph theory (which dates back to 1 8th century mathematician Leonhard Euler), are well-established. Graph theory has revealed common principles that define small-world networks - in which clusters of sparse connections allow short paths between any two nodes - in applications as diverse as social circles, epidemiology and transportation routes. What is new - and exciting - is the application of network analytic approaches to neuroscience: over the past decade, graph theory has been applied to the wiring diagrams or connectomes within brains. Specific neuroscience applications span a range of scales: thousands of connections between hundreds of neurons in the simple roundworm, Caenorhabditis elegans; hundreds of connections between dozens of functional areas of the human brain; and potentially even the trillions of connections between billions of neurons in the human brain. Sporns provides a comprehensive, tour-de-force overview of the cutting edge of the application of network science to neuroscience. This is a book that everyone with an interest in brain function should read. It provides a grand overview of a field that will undoubtedly hold a central position in the future of neuroscience - if it has not already taken that position now. The scope of the book is enormous, yet relatively self-contained. Sporns masterfully reviews and explains core concepts from many neuroscientific, psychological, mathematical, physical, and engineering disciplines in a way which is at once accessible to nonspecialists and, in most cases, does not sacrifice correctness or rigour to achieve that goal. The use of clear, natural language explanations in place of mathematical in order to appeal to a broader authence is commendable. Despite the inherent difficulty of such an undertaking, it is exactly the authence members who might be put off by too many equations who are most likely to benefit immediately from considering the approaches reviewed by Sporns. Chapter 1 provides an appetizer of sorts, briefly and clearly outlining a compelling argument for why one might wish to read the rest of the book. Chapters 2 and 3 provide the necessary background in network theory and neuroscience methods, respectively, to make sense of the rest of the book. The exposition of this material is clear, well written, and given in a wonderfully informal style - as though being guided through a new field over drinks by a very patient and knowledgeable colleague. Chapters 4-7 outline the application of network theoretic approaches to connections within anatomical circuits in the brain. Sporns is at the top of his game here and writes as one who has masterful knowledge of both the current state of the field and where it is likely to go. Particularly impressive is Sporns' very careful treatment of evolution in Chapter 7; he provides a network-theoretic framework for thinking about structural evolution in the brain by calling on a deep, broad range of supporting literature and making the full argument in terms of modern evolutionary theory. …
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".