Open-source numerical simulation tool for two-dimensional neural fields involving finite axonal transmission speed
Bibliographic record
Abstract
This work aims to provide an open source and cross-platform simulation tool that integrates numerically integral-differential equations in a two-dimensional quadratic spatial domain with periodic boundary conditions and finite axonal transmission speed inducing space-dependent delays.. The term I denotes the external stimulus, K is the synaptic connectivity kernel and S is the firing rate. Finite axonal transmission speed c induces space-dependent delays. Conventional implementations of two-dimensional integration with space-dependent delays are rather slow due to the missing convolution in the integral. It has been shown in a previous work that this unfortunate property can be overcome by introducing a spatio-temporal kernel, rendering the integral into a spatial integral and an integral over delays. Four major aspects accelerate the integration speed: (a) the underlying numerical method is expedited with a fast Fourier transform in space (b) the simulator writes and executes its own code based on interface selections to efficiently calculate and display only the user-defined features (c) the displayed matrix is put onto the running system's graphics processing unit for hardware acceleration of the visualization (d) reduced rate of GPU uploads optimized for visual perception. The simulator gives the user full control of the variables by permitting free choice of all variables through a text-based interface. Two dimensional field matrices can be displayed in rich detail in a three-dimensional plot. The visualization of field data is easily modified by a keypress, performing functions such as moving, rotating, zooming and changing colors and axis limits. Images and videos can be saved and simulations can be paused and resumed using the keyboard. The software is open-source and written in Python.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.030 | 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".