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一种用于振动感受器突触后神经元频率选择性的线性化建模框架。

A linearized modeling framework for the frequency selectivity in neurons postsynaptic to vibration receptors.

作者信息

Gao Tian, Deng Bin, Wang Jiang, Yi Guosheng

机构信息

School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072 China.

出版信息

Cogn Neurodyn. 2024 Aug;18(4):2061-2075. doi: 10.1007/s11571-024-10070-8. Epub 2024 Feb 20.

Abstract

Vibration is an indispensable part of the tactile perception, which is encoded to oscillatory synaptic currents by receptors and transferred to neurons in the brain. The A2 and B1 neurons in the drosophila brain postsynaptic to the vibration receptors exhibit selective preferences for oscillatory synaptic currents with different frequencies, which is caused by the specific voltage-gated Na and K currents that both oppose the variations in membrane potential. To understand the peculiar role of the Na and K currents in shaping the filtering property of A2 and B1 neurons, we develop a linearized modeling framework that allows to systematically change the activation properties of these ionic channels. A data-driven conductance-based biophysical model is used to reproduce the frequency filtering of oscillatory synaptic inputs. Then, this data-driven model is linearized at the resting potential and its frequency response is calculated based on the transfer function, which is described by the magnitude-frequency curve. When we regulate the activation properties of the Na and K channels by changing the biophysical parameters, the dominant pole of the transfer function is found to be highly correlated with the fluctuation of the active current, which represents the strength of suppression of slow voltage variation. Meanwhile, the dominant pole also shapes the magnitude-frequency curve and further qualitatively determines the filtering property of the model. The transfer function provides a parsimonious description of how the biophysical parameters in Na and K channels change the inhibition of slow variations in membrane potential by Na and K currents, and further illustrates the relationship between the filtering properties and the activation properties of Na and K channels. This computational framework with the data-driven conductance-based biophysical model and its linearized model contributes to understanding the transmission and filtering of vibration stimulus in the tactile system.

摘要

振动是触觉感知不可或缺的一部分,它由感受器编码为振荡性突触电流,并传递至大脑中的神经元。果蝇大脑中与振动感受器形成突触后的A2和B1神经元,对不同频率的振荡性突触电流表现出选择性偏好,这是由特定的电压门控钠电流和钾电流引起的,这两种电流都对抗膜电位的变化。为了理解钠电流和钾电流在塑造A2和B1神经元滤波特性中的特殊作用,我们开发了一个线性化建模框架,该框架允许系统地改变这些离子通道的激活特性。基于数据驱动的基于电导的生物物理模型用于再现振荡性突触输入的频率滤波。然后,这个基于数据驱动的模型在静息电位处进行线性化,并根据传递函数计算其频率响应,传递函数由幅值-频率曲线描述。当我们通过改变生物物理参数来调节钠通道和钾通道的激活特性时,发现传递函数的主导极点与有源电流的波动高度相关,有源电流的波动代表了对缓慢电压变化的抑制强度。同时,主导极点也塑造了幅值-频率曲线,并进一步定性地决定了模型的滤波特性。传递函数简洁地描述了钠通道和钾通道中的生物物理参数如何通过钠电流和钾电流改变对膜电位缓慢变化的抑制作用,并进一步阐明了滤波特性与钠通道和钾通道激活特性之间的关系。这个具有基于数据驱动的基于电导的生物物理模型及其线性化模型的计算框架,有助于理解触觉系统中振动刺激的传递和滤波。

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