Multiplierless Implementation of Fitz-Hugh Nagumo (FHN) Modeling Using CORDIC Approach
Bibliographic record
Abstract
The study, simulation, and implementation of neural behavior in the human brain are central goals of neuromorphic engineering. By integrating various scientific fields, we present a hardware solution based on neuronal cell mechanisms that can emulate such a nature-inspired system. This article presents a Fitz-Hugh Nagumo (FHN) neuron implemented using COordinate Rotation DIgital Computer (CORDIC), which accurately reproduces various patterns of the original FHN neuron model. We propose a modification to the original nonlinear term using a CORDIC IP-Core, resulting in high matching accuracy and low computational error. The proposed model is validated through time domain and dynamic analysis, which demonstrates its high accuracy and low error in reproducing all features of the FHN model. For large scale neuron implementations, we present an efficient digital hardware solution based on the resource sharing techniques. The hardware is implemented on Field-Programmable Gate Array (FPGA) using Hardware Description Language (HDL), as a proof of concept. The results from the hardware implementation show that the proposed model uses only 1% of the resources available on a Virtex 4 FPGA board. Additionally, the static timing analysis shows that the circuit can operate at a maximum frequency of 320 MHz.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".