Connecting psychophysical performance to neuronal response properties I: Discrimination of suprathreshold stimuli.
Connecting psychophysical performance to neuronal response properties I: Discrimination of suprathreshold stimuli.
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DOI：
10.1167/15.6.8
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发表时间：
2015-05
影响因子：
1.8
通讯作者：
K. May;J. Solomon
中科院分区：
文献类型：
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作者：
K. May;J. Solomon
感觉神经科学的主要目标之一是了解生物体的感知能力如何与潜在的生理学相关。为此，我们推导出方程来估计最好的心理物理的歧视性能，给定的属性的神经元携带的感觉代码。我们建立了一个通用的感觉编码模型的神经元的特点是他们的调谐功能的刺激和随机过程，产生尖峰。调谐函数是高斯函数或S形（Naka-Rushton）函数，尖峰信号是使用泊松尖峰过程产生的，其速率由神经元之间共享的乘性的、伽马分布的增益信号调制。这种双重随机过程产生了现实水平的神经元变异性和现实的相关性结构的人口。使用Fisher信息作为模型解码精度的密切近似，我们推导出根据神经元参数预测模型鉴别性能的方程。然后，我们使用Monte Carlo模拟验证了我们的方程的准确性。我们的工作有两大好处。首先，我们可以通过评估简单的方程来快速计算生理上合理的群体编码模型的性能，这使得模型很容易拟合心理物理数据。其次，这些方程揭示了心理物理辨别能力与神经元群体参数之间的一些非常直接的关系，从而深入了解了生物体感知能力与这些能力所依赖的神经元属性之间的关系。
One of the major goals of sensory neuroscience is to understand how an organism's perceptual abilities relate to the underlying physiology. To this end, we derived equations to estimate the best possible psychophysical discrimination performance, given the properties of the neurons carrying the sensory code.We set up a generic sensory coding model with neurons characterized by their tuning function to the stimulus and the random process that generates spikes. The tuning function was a Gaussian function or a sigmoid (Naka-Rushton) function.Spikes were generated using Poisson spiking processes whose rates were modulated by a multiplicative, gamma-distributed gain signal that was shared between neurons. This doubly stochastic process generates realistic levels of neuronal variability and a realistic correlation structure within the population. Using Fisher information as a close approximation of the model's decoding precision, we derived equations to predict the model's discrimination performance from the neuronal parameters. We then verified the accuracy of our equations using Monte Carlo simulations. Our work has two major benefits. Firstly, we can quickly calculate the performance of physiologically plausible population-coding models by evaluating simple equations, which makes it easy to fit the model to psychophysical data. Secondly, the equations revealed some remarkably straightforward relationships between psychophysical discrimination performance and the parameters of the neuronal population, giving deep insights into the relationships between an organism's perceptual abilities and the properties of the neurons on which those abilities depend.