An Agent-Based Simulation of the Nervous System’s Reflex Response to Pain
Bibliographic record
Abstract
The reflex arc is an important process of the nervous system that helps protect the body from damage by responding instantaneously to external stimuli, and has undergone a lot of research in the last century. This arc has been represented and taught using a variety of methods including diagrams, graphs, animations and mathematical equations [1, 2]. A missing connection in these methods can be seen between the complex mathematics used for research and a suitable visual representation for easier learning and understanding of the topic. A suitable combination could provide a powerful tool useful in both research and education. The Reflex Arc simulation, part of the Lindsay Virtual Human Project, proposes a novel way of combining a number of resources into one multi-scale model of the nervous system’s reflex response to pain. Among some of the advantages of the model are its high quality visual components with a level of abstraction that still keeps them easily recognizable, and the user’s ability to freely navigate around the three-dimensional space in which it is located and watch the path of the reflex arc from a distant or detailed perspective. The interaction processes are modeled following an agent-based programming paradigm [3]. Each agent, for instance the nociceptors in sensory neurons, act in accordance with the sharp object that triggers a response when the two collide. The agent-based model representation allows for user and global parameter changes while the simulation is running. This project and its future additions provides contents for a novel set of computational tools that can be used for a variety of research and educational purposes.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".