FPGA based pipelined architecture for action potential simulation in biological neural systems
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a hardware based approach to simulate action potential of large numbers of somas within a biological neural network. At the proposed method multiple processors can work in parallel to increase processing power as required. The high speed pipelined architecture for each processor provides the computation speed of one soma per clock ratio and with multiple processors higher speeds are achievable. The design is highly scalable such that the number of cells in the model is limited only by the available memory size. Compartmental approach and Hodgkin-Huxley methods are used as simulation models in our studies. The approach is verified in MATLAB and is synthesized for Xilinx V5-110t-1 as the target FPGA. While not dependent on particular IP cores, the whole implementation is based on Xilinx IP cores including IEEE-754 64-bit floating-point adder and multiplier cores.
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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