Digital FPGA implementation of spontaneous astrocyte signalling
Bibliographic record
Abstract
Summary Astrocytes are the most abundant type of glial cells in the central nervous system (CNS). These non‐neuronal cells are able to regulate the neurons activity in the different parts of brain tissue by calcium waves generation in its internal space. Moreover, astrocytes interact with neurons and modulate the spiking activity of them. In this paper, a set of piecewise linear estimations of a three‐dimensional spontaneous astrocyte model are presented for digital FPGA realization. This leads to achieve a high‐speed and low‐cost system in large‐scale implementation. In this approach, the three‐dimensional original model is converted to a two‐dimensional one and the hardware overhead have been reduced, significantly due to eliminating the large number of multiplications in the original astrocyte model. Simulation results in MATLAB demonstrate that our method can mimic the original calcium waves in high degree of similarity. To validate our method in case of hardware, the proposed model has been tested and simulated in Modelsim software and also implemented on Spartan3 XC3S50 (TQ144) FPGA board. Hardware realization results show that the proposed model has high similarity by the simulation outputs. Consequently, this reduced‐model of astrocyte can be used in large‐scale networks because of its low‐cost hardware and high‐speed system.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".