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A Phenomenological Model of the Electrically Stimulated Auditory Nerve Fiber: Temporal and Biphasic Response Properties

机译:电刺激听觉神经纤维的现象学模型:时间和双相响应特性。

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摘要

We present a phenomenological model of electrically stimulated auditory nerve fibers (ANFs). The model reproduces the probabilistic and temporal properties of the ANF response to both monophasic and biphasic stimuli, in isolation. The main contribution of the model lies in its ability to reproduce statistics of the ANF response (mean latency, jitter, and firing probability) under both monophasic and cathodic-anodic biphasic stimulation, without changing the model's parameters. The response statistics of the model depend on stimulus level and duration of the stimulating pulse, reproducing trends observed in the ANF. In the case of biphasic stimulation, the model reproduces the effects of pseudomonophasic pulse shapes and also the dependence on the interphase gap (IPG) of the stimulus pulse, an effect that is quantitatively reproduced. The model is fitted to ANF data using a procedure that uniquely determines each model parameter. It is thus possible to rapidly parameterize a large population of neurons to reproduce a given set of response statistic distributions. Our work extends the stochastic leaky integrate and fire (SLIF) neuron, a well-studied phenomenological model of the electrically stimulated neuron. We extend the SLIF neuron so as to produce a realistic latency distribution by delaying the moment of spiking. During this delay, spiking may be abolished by anodic current. By this means, the probability of the model neuron responding to a stimulus is reduced when a trailing phase of opposite polarity is introduced. By introducing a minimum wait period that must elapse before a spike may be emitted, the model is able to reproduce the differences in the threshold level observed in the ANF for monophasic and biphasic stimuli. Thus, the ANF response to a large variety of pulse shapes are reproduced correctly by this model.
机译:我们提出了电刺激听觉神经纤维(ANFs)的现象学模型。该模型独立地重现了ANF对单相和双相刺激的响应的概率和时间性质。该模型的主要贡献在于它能够在不改变模型参数的情况下,在单相和阴极-阳极双相激励下重现ANF响应的统计信息(平均潜伏期,抖动和发射概率)。该模型的响应统计取决于刺激水平和刺激脉冲的持续时间,再现了在ANF中观察到的趋势。在双相刺激的情况下,模型会重现伪单相脉冲形状的影响,还会重现对刺激脉冲的相间间隙(IPG)的依赖性,这种影响会定量地重现。使用唯一确定每个模型参数的过程将模型拟合到ANF数据。因此,可以快速参数化大量神经元,以重现一组给定的响应统计分布。我们的工作扩展了随机渗漏积分和放电(SLIF)神经元,这是一种经过充分研究的电刺激神经元现象学模型。我们扩展了SLIF神经元,以便通过延迟尖峰时刻来产生逼真的延迟分布。在此延迟期间,阳极电流可消除尖峰。通过这种方式,当引入相反极性的尾随相位时,降低了模型神经元对刺激做出响应的可能性。通过引入在发出尖峰之前必须经过的最小等待时间,该模型能够重现在ANF中针对单相和双相刺激观察到的阈值水平的差异。因此,通过该模型可以正确地再现对多种脉冲形状的ANF响应。

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