High Speed and Low Digital Resources Implementation of Hodgkin-Huxley Neuronal Model Using Base-2 Functions
Bibliographic record
Abstract
Neurons are the basic blocks in the Central Nervous System (CNS). Simulation and hardware realization of these blocks are vital in neuromorphic engineering. This paper presents a set of multiplierless mathematical equations based on 2Xterms to achieve a low-cost, high-speed, and high-accuracy digital implementation of Hodgkin-Huxley (HH) neuron model. The HH model is the most complicated and high-accuracy among the mathematical neuron models. The proposed model can reproduce spiking behaviors of the original HH model with high precision. To validate the mathematical simulation results, the proposed model has been synthesized and implemented on Field-Programmable Gate Array (FPGA) development board. Hardware synthesis and physical implementations reveal that the biological behavior of different spiking patterns can be reproduced with higher performance and significantly lower implementation costs compared with the original HH model. Also, in this approach the maximum frequency of 200 MHz is achievable which is valuable in comparison with other similar works.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".