RECORD
Computational Models for SA, RA, PC Afferent to Reproduce Neural Responses to Dynamic Stimulus Using FEM Analysis and a Leaky Integrate-and-Fire Model
Robotics · Submitted 1 Aug 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim From the result, the computational models well reproduced the neural responses to vibration stimuli such as sinusoidal, diharmonic, and noise stimuli and compare favorably with the similar computational models that can simulate the responses to vibration stimuli.
The compiled record
The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.
Open the compiled record
The abstract, sentence by sentence
Tactile afferents such as (RA), and Pacinian (PC) afferents that respond to external stimuli enable complicated actions such as grasping, stroking and identifying an object. To understand the tactile sensation induced by these actions deeply, the activities of the tactile afferents need to be revealed. For this purpose, we develop a computational model for each tactile afferent for vibration stimuli, combining finite element analysis finite element method (FEM) analysis and a leaky integrate-and-fire model that represents the neural characteristics. This computational model can easily estimate the neural activities of the tactile afferents without measuring biological data. Skin deformation calculated using FEM analysis is substituted into the integrate-and-fire model as current input to calculate the membrane potential of each tactile afferent. We optimized parameters in the integrate-and-fire models using reported biological data. Then, we calculated the responses of the numerical models to sinusoidal, diharmonic, and white-noise-like mechanical stimuli to validate the proposed numerical models. From the result, the computational models well reproduced the neural responses to vibration stimuli such as sinusoidal, diharmonic, and noise stimuli and compare favorably with the similar computational models that can simulate the responses to vibration stimuli.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Tactile afferents such as (RA), and Pacinian (PC) afferents that respond to external stimuli enable complicated actions such as grasping, stroking and identifying an object. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s1 | To understand the tactile sensation induced by these actions deeply, the activities of the tactile afferents need to be revealed. | background 0.88 | 0.06 | 0.02 | asserted 0.95 |
| s2 | For this purpose, we develop a computational model for each tactile afferent for vibration stimuli, combining finite element analysis finite element method (FEM) analysis and a leaky integrate-and-fire model that represents the neural characteristics. | contribution 0.54 | 0.95 | 0.05 | asserted 1.00 |
| s3 | This computational model can easily estimate the neural activities of the tactile afferents without measuring biological data. | contribution 0.71 | 0.56 | 0.16 | asserted 0.85 |
| s4 | Skin deformation calculated using FEM analysis is substituted into the integrate-and-fire model as current input to calculate the membrane potential of each tactile afferent. | method 1.00 | 0.79 | 0.04 | asserted 1.00 |
| s5 | We optimized parameters in the integrate-and-fire models using reported biological data. | method 1.00 | 0.92 | 0.03 | asserted 1.00 |
| s6 | Then, we calculated the responses of the numerical models to sinusoidal, diharmonic, and white-noise-like mechanical stimuli to validate the proposed numerical models. | method 0.98 | 0.96 | 0.05 | asserted 0.92 |
| s7 | From the result, the computational models well reproduced the neural responses to vibration stimuli such as sinusoidal, diharmonic, and noise stimuli and compare favorably with the similar computational models that can simulate the responses to vibration stimuli. | result 0.94 | 0.92 | 0.89 | asserted 1.00 |
What was examined, and what was not
- arXiv listing abstract v1
- full text
- figures and tables
- references and citing sentences
- relations to other papers
- P abstract
- S sections
- T body sentences
- C citations
- R cross-paper relations
The paper
Original abstract
Tactile afferents such as (RA), and Pacinian (PC) afferents that respond to external stimuli enable complicated actions such as grasping, stroking and identifying an object. To understand the tactile sensation induced by these actions deeply, the activities of the tactile afferents need to be revealed. For this purpose, we develop a computational model for each tactile afferent for vibration stimuli, combining finite element analysis finite element method (FEM) analysis and a leaky integrate-and-fire model that represents the neural characteristics. This computational model can easily estimate the neural activities of the tactile afferents without measuring biological data. Skin deformation calculated using FEM analysis is substituted into the integrate-and-fire model as current input to calculate the membrane potential of each tactile afferent. We optimized parameters in the integrate-and-fire models using reported biological data. Then, we calculated the responses of the numerical models to sinusoidal, diharmonic, and white-noise-like mechanical stimuli to validate the proposed numerical models. From the result, the computational models well reproduced the neural responses to vibration stimuli such as sinusoidal, diharmonic, and noise stimuli and compare favorably with the similar computational models that can simulate the responses to vibration stimuli.
Human review
Discussion
Know something about this paper? Leave a comment, or highlight any passage above to comment on it.