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Coding source localization through inter-spike delay: modelling a cluster of Pacinian Corpuscles using time-division multiplexing approach.
Somatosensory & Motor Research ( IF 1.3 ) Pub Date : 2020-02-17 , DOI: 10.1080/08990220.2020.1726739
Madhan Kumar Vasudevan 1 , Venkatraman Sadanand 2 , Manivannan Muniyandi 1 , Mandayam A Srinivasan 3
Affiliation  

The Pacinian Corpuscle (PC) is the most sensitive mechanoreceptor in the human body found in clusters of two or three. We extended our previous model of an isolated-PC to a cluster-PC focussing on relative spike delay and displacement threshold for understanding how the stimulus location is coded. In our model, two PCs with Gaussian overlapping receptive fields are arranged beneath the skin model. For a spatiotemporal stimulus (vibration), the model response is proposed to be a time-division multiplexing of responses from two PCs within the cluster. While the spike rate characteristics and pole-zero plot of cluster-PC model show similarities with the isolated-PC model, the frequency response shows ripples after 1 kHz as the distance (d) between the PCs increases. The stimulus location [Formula: see text] and d influence the relative spike delay and the displacement threshold, but not the spike rate. The novel contributions from our model include prediction of (i) relative spike delay for various d, stimulus frequency (f), and ψ, (ii) spike rate characteristics for varying f, and (iii) displacement threshold curve as a function of frequency for various d. Although the physiological validation of the novel predictions is impractical, we have validated the relative spike delay and the displacement threshold curves with experimental data in the literature.

中文翻译:

通过脉冲间延迟编码源定位:使用时分复用方法对 Pacinian 小体集群进行建模。

Pacinian 小体 (PC) 是人体中最敏感的机械感受器,以两个或三个为一组。我们将之前的孤立 PC 模型扩展到集群 PC,重点关注相对尖峰延迟和位移阈值,以了解刺激位置的编码方式。在我们的模型中,两个具有高斯重叠感受野的 PC 排列在皮肤模型下方。对于时空刺激(振动),建议模型响应是来自集群内两台 PC 的响应的时分复用。虽然集群 PC 模型的尖峰率特性和零极点图显示出与孤立 PC 模型的相似性,但随着 PC 之间的距离 (d) 增加,频率响应在 1 kHz 后显示出波纹。刺激位置[公式:见正文] 和 d 影响相对尖峰延迟和位移阈值,但不影响尖峰率。我们模型的新贡献包括预测 (i) 各种 d、刺激频率 (f) 和 ψ 的相对尖峰延迟,(ii) 变化 f 的尖峰率特性,以及 (iii) 作为频率函数的位移阈值曲线对于各种 d. 虽然新预测的生理验证是不切实际的,但我们已经用文献中的实验数据验证了相对尖峰延迟和位移阈值曲线。(iii) 位移阈值曲线作为各种 d 的频率函数。虽然新预测的生理验证是不切实际的,但我们已经用文献中的实验数据验证了相对尖峰延迟和位移阈值曲线。(iii) 位移阈值曲线作为各种 d 的频率函数。虽然新预测的生理验证是不切实际的,但我们已经用文献中的实验数据验证了相对尖峰延迟和位移阈值曲线。
更新日期:2020-02-17
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