Bibliographic record
Abstract
Abstract This is an introductory book for computational neuroscience. The book starts with a high-level overview and some fundamental questions about brain theories, a brief discussion about the role of modeling, and some other basic facts from neuroscience. The book also reviews essential scientific programming in Python and the basic mathematical and statistical concept used in the book. The following part of the book focuses on basic mechanisms and modeling of single neurons or population averages. This starts from detailed discussion of changes in the membrane potentials through ion channels, spike generations, and synaptic plasticity, with increasingly abstractions in the following chapters. After this, the information processing capabilities of basic networks are described, including feedforward and competitive recurrent networks. The last part of the book describes some examples of combining such elementary networks as well as some examples of more system-level models of the brain. This new edition of my book incorporates recent lessons from deep learning. While there are excellent books on deep learning, the emphasis here is their connection to brain processing. An important aspect is thereby the concepts of representational learning and computation with uncertainties. Also, I now included gated recurrent neural networks that are becoming an important fundamental mechanism when thinking about brain processing. While we will not be able to dive into all the recent progress, I hope that the text will guide further specific studies and research.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.035 | 0.023 |
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".