A Simple Lapicque Neuron Emulator
Bibliographic record
Abstract
Neuron models such as Leaky Integrate and Fire (Lapicque Neuron) model, Hodgkin Huxley Model, Fitzhugh-Nagumo Model, and Izhikevich neuron model are commonly used in neuronal studies.Nobel laureate Hudgkin-Hugsley neuron model is quite complex despite of being accurate.That's why simplified neuron models such as Fitzhugh-Nagumo neuron model is commonly used in studies.Lapicque Neuron is the first neuron model suggested in Literature.It had been reported by Lapicque in 1907, it is easy to understand, and it is still commonly used for neural studies for its simplicity.Neuron emulator circuits are used for education and research purposes.They can be made using individual electronics components or VLSI circuits.In this study, a Lapicque Neuron emulator circuit topology is introduced.It is simple enough to be made by students and it can be made using cheap off-the shelves components in a short time.Its circuit analysis is also given.It is experimentally shown that the emulator is able to mimic activation potentials well.It is suggested that the neuron emulator can be used for not only educational purposes in Biomedical Engineering Courses but also neuronal studies to show and prove concepts.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".