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Firing-rate models capture essential response dynamics of LGN relay cells

机译：射速模型捕获LGN中继单元的基本响应动态

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摘要

Firing-rate models provide a practical tool for studying signal processing in the early visual system, permitting more thorough mathematical analysis than spike-based models. We show here that essential response properties of relay cells in the lateral geniculate nucleus (LGN) can be captured by surprisingly simple firing-rate models consisting of a low-pass filter and a nonlinear activation function. The starting point for our analysis are two spiking neuron models based on experimental data: a spike-response model fitted to data from macaque (Carandini et al. J. Vis., 20(14), 1-2011, 2007), and a model with conductance-based synapses and afterhyperpolarizing currents fitted to data from cat (Casti et al. J. Comput. Neurosci., 24(2), 235-252,2008). We obtained the nonlinear activation function by stimulating the model neurons with stationary stochastic spike trains, while we characterized the linear filter by fitting a low-pass filter to responses to sinusoidally modulated stochastic spike trains. To account for the non-Poisson nature of retinal spike trains, we performed all analyses with spike trains with higher-order gamma statistics in addition to Poissonian spike trains. Interestingly, the properties of the low-pass filter depend only on the average input rate, but not on the modulation depth of sinusoidally modulated input. Thus, the response properties of our model are fully specified by just three parameters (low-frequency gain, cutoff frequency, and delay) for a given mean input rate and input regularity. This simple firing-rate model reproduces the response of spiking neurons to a step in input rate very well for Poissonian as well as for non-Poissonian input. We also found that the cutoff frequencies, and thus the filter time constants, of the rate-based model are unrelated to the membrane time constants of the underlying spiking models, in agreement with similar observations for simpler models.
机译：发射速率模型为研究早期视觉系统中的信号处理提供了一种实用的工具，与基于尖峰的模型相比，可以进行更彻底的数学分析。我们在这里显示，可以通过令人惊讶的简单的由低通滤波器和非线性激活函数组成的触发速率模型来捕获外侧膝状核（LGN）中中继细胞的基本响应特性。我们的分析起点是两个基于实验数据的尖峰神经元模型：一个针对猕猴数据的尖峰响应模型（Carandini等人，J。Vis。，20（14），1-2011，2007），以及一个具有基于电导的突触和超极化后电流适合猫数据的模型（Casti等，J。Comput。Neurosci。，24（2），235-252,2008）。我们通过用固定的随机峰值序列刺激模型神经元来获得非线性激活函数，而我们通过将低通滤波器拟合对正弦调制的随机峰值序列的响应来表征线性滤波器。为了说明视网膜峰值序列的非泊松性质，除了泊松峰值序列之外，我们还使用具有更高阶伽玛统计量的峰值序列进行了所有分析。有趣的是，低通滤波器的属性仅取决于平均输入速率，而不取决于正弦调制输入的调制深度。因此，对于给定的平均输入速率和输入规律性，我们的模型的响应特性仅由三个参数（低频增益，截止频率和延迟）完全指定。对于Poissonian以及非Poissonian输入，此简单的激发速率模型都能很好地再现尖峰神经元对输入速率阶跃的响应。我们还发现，基于速率的模型的截止频率，进而是滤波器的时间常数，与底层尖峰模型的膜时间常数无关，这与对简单模型的类似观察结果一致。

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  • 来源
    《Journal of Computational Neuroscience》 |2013年第3期|359-375|共17页
  • 作者

    Thomas Heiberg; Birgit Kriener; Tom Tetzlaff; Alex Casti; Gaute T. Einevoll; Hans E. Plesser;

  • 作者单位

    Department of Mathematical Sciences and Technology,Norwegian University of Life Sciences, P.O. Box 5003,1432 As, Norway;

    Department of Mathematical Sciences and Technology,Norwegian University of Life Sciences, P.O. Box 5003,1432 As, Norway;

    Institute of Neuroscience and Medicine (INM-6), Research Center Jueilich, Jiilich, Germany;

    Department of Mathematics, Gildart-Haase School of Computer Sciences and Engineering, Fairleigh Dickinson University,Teaneck, NJ, USA;

    Department of Mathematical Sciences and Technology,Norwegian University of Life Sciences, P.O. Box 5003,1432 As, Norway;

    Department of Mathematical Sciences and Technology,Norwegian University of Life Sciences, P.O. Box 5003,1432 As, Norway;

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  • 正文语种 eng
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  • 关键词

    LGN; Retina; Visual system; Rate model; Linear-nonlinear model;

    机译：LGN;视网膜;视觉系统;费率模型;线性-非线性模型;

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