A Conceptual Model of Micro Inertial Sensor Mimicking Amplifying Mechanism of the Hair Cells
Bibliographic record
Abstract
The inner ear hair cells, the receptors sensing mechanical stimuli such as acoustic vibration and acceleration, achieve remarkably high sensitivity to miniscule stimuli by selectively amplifying small inputs. The gating springs hypothesis proposes that a phenomenon called negative stiffness is responsible for the nonlinear sensitivity. According to the hypothesis, the bundle becomes more sensitive in certain region as its stiffness changes due to the opening or closing of transduction channels, which in turn exert force in the same direction of the bundle’s displacement. In this study, we developed a conceptual model of an inertial sensor inspired by the inner ear hair cells, focusing on the hair cell’s amplifying mechanism known as negative stiffness. The negative stiffness was applied to a simple mass-spring-damper system with nonlinear spring derived from gating springs hypothesis. Sinusoidal stimuli of 0.1Hz~10Hz with magnitude of 1pN to 1000pN were applied to the system to match the dynamic range of vestibular organs. Simulation on this nonlinear model was performed on MATLAB, and power transfers and sensitivities in both transient and steady states were obtained and compared with those from the system with linear spring. Parameters were chosen in relation to those of the hair bundle to reproduce operating conditions of both the hair cells and micro inertial sensors. The suggested model displayed compressive nonlinear sensitivity resulting from selective amplification of smaller stimuli despite the energy loss due to large viscous damping typical in micro systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".