A novel scalable parallel architecture for biological neural simulations
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a scalable hierarchical architecture for accelerating simulations of large-scale biological neural systems on FPGA-based platforms. The architecture provides a high degree of flexibility to optimize the parallelization ratio based on available hardware resources and model specifications such as complexity of dendritic trees. The proposed addressing scheme, design modularity and data process localization allowing the whole system to extend over multiple FPGA platforms to simulate a very large biological neural system. Compartmental approach and Hodgkin-Huxley methods are used as simulation models in our studies. The architecture is verified in MATLAB and implemented based on four types of hardware modules, with two modules synthesized on Xilinx XC5VLX110T-1 devices.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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