Sensory Input–Based Adaptation and Brain Architecture
Bibliographic record
Abstract
It is well established that brain development depends on the interaction between the basic components of the nervous system (nature) and the environment (nurture). This interaction, however, relies on a number of rules that could modify not only the organization of neural systems, but also their function. In this chapter, we report results on the plasticity of the visual system in animal and human models, using a variety of methodological approaches. In particular, we describe major findings regarding plasticity that result from modifications of the visual input through lesions in the various stages of the visual pathway (peripheral and central). Possible mechanisms for such neural reorganization are also discussed . NATURE VERSUS NURTURE: ENVIRONMENTAL EFFECTS ON BRAIN PLASTICITY One of the oldest issues in modern psychology and biology concerns the nature versus nurture conundrum. Miscellaneous inquiries have been explored in this topic, such as “to what extent can genetic dispositions endow behaviors?” and “to what degree can the environment shape these?” It is well established that brain development depends on the interaction between the basic components of the nervous system (nature) and the stimulating environment (nurture). However, this interaction relies on a number of rules that could modify not only the organization of neural systems, but also their function. As we consider the main principles of evolution, we focus on the characteristics of the brain that are inheritable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".