Optimizations for neuron time warp(NTW) for stochastic reaction-diffusion models of neurons
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The intracellular calcium signaling pathways of a neuron consist of biochemical reactions along with molecular diffusion. It is known that stochastic discrete event simulation of these pathways provides a more detailed understanding of the pathways than deterministic simulators because they capture behavior at a molecular level. Our research employs a parallel discrete event simulation simulator, Neuron Time Warp (NTW), which is intended for use for the simulation of neurons. In previous work we built a discrete event Ca2+ wave model. However, we did not achieve the expected performance because of an imbalance in the computation between the area of the neuron covered by the Ca2+ wave and the remaining area of the neuron. In this paper we describe a dynamic load balancing algorithm and a dynamic window control algorithm for NTW. We make use of Q-learning to determine the basic parameters of the algorithm. Using this algorithm we obtained an improvement in the performance of the simulator of up to 30%.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it