Information and discriminability as measures of reliability of sensory coding.
Information and discriminability as measures of reliability of sensory coding.
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DOI：
10.1371/journal.pone.0001328
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发表时间：
2007-12-19
期刊：
影响因子：
3.7
通讯作者：
Warzecha AK
中科院分区：
文献类型：
--
作者：
Grewe J;Weckström M;Egelhaaf M;Warzecha AK
反应可变性是神经编码中的一个基本问题，因为它限制了所有的信息处理。在不同的研究中，神经元编码的可靠性被用不同的方法量化。在大多数情况下，很大程度上还不清楚结论在多大程度上取决于应用的可靠性衡量标准，这使得跨研究的比较几乎是不可能的。我们证明，即使应用于同一组数据，不同的可靠性度量也可以导致非常不同的结论：特别是，我们应用了信息理论度量(香农信息容量和Kullback-Leibler散度)以及源自信号检测理论的判别度量来处理苍蝇光感受器的反应，这代表了一个良好的感官信息处理模型系统。我们用白噪声来刺激光感受器，调制不同对比度的光强度波动。令人惊讶的是，信号检测方法即使在响应信噪比(SNR)远低于1的情况下也能安全地识别光感受器响应，而Shannon信息量和Kullback-Leibler散度表明性能非常低。因此，应用不同的测量可能导致对系统编码性能的非常不同的解释。由于与信号检测方法相比灵敏度较低，信息论方法高估了内部噪声源，低估了光子散粒噪声的重要性。我们强调，没有一种使用的测量方法，而且很可能没有其他单独的测量方法，允许对神经元的编码特性进行无偏见的估计。因此，需要根据科学问题和所分析的神经元的功能背景来选择应用的测量方法。
Response variability is a fundamental issue in neural coding because it limits all information processing. The reliability of neuronal coding is quantified by various approaches in different studies. In most cases it is largely unclear to what extent the conclusions depend on the applied reliability measure, making a comparison across studies almost impossible. We demonstrate that different reliability measures can lead to very different conclusions even if applied to the same set of data: in particular, we applied information theoretical measures (Shannon information capacity and Kullback-Leibler divergence) as well as a discrimination measure derived from signal-detection theory to the responses of blowfly photoreceptors which represent a well established model system for sensory information processing. We stimulated the photoreceptors with white noise modulated light intensity fluctuations of different contrasts. Surprisingly, the signal-detection approach leads to a safe discrimination of the photoreceptor response even when the response signal-to-noise ratio (SNR) is well below unity whereas Shannon information capacity and also Kullback-Leibler divergence indicate a very low performance. Applying different measures, can, therefore, lead to very different interpretations concerning the system's coding performance. As a consequence of the lower sensitivity compared to the signal-detection approach, the information theoretical measures overestimate internal noise sources and underestimate the importance of photon shot noise. We stress that none of the used measures and, most likely no other measure alone, allows for an unbiased estimation of a neuron's coding properties. Therefore the applied measure needs to be selected with respect to the scientific question and the analyzed neuron's functional context.
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