泊松源独立观测值的最优整合。
Optimal integration of independent observations from Poisson sources.
作者信息
Dai Huanping, Buss Emily
机构信息
Department of Speech, Language, and Hearing Sciences, University of Arizona, Tucson, Arizona 85721
Department of Otolaryngology/Head and Neck Surgery, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
出版信息
J Acoust Soc Am. 2015 Jan;137(1):EL20-5. doi: 10.1121/1.4903228.
The optimal integration of information from independent Poisson sources (such as neurons) was analyzed in the context of a two-interval, forced-choice detection task. When the mean count of the Poisson distribution is above 1, the benefit of integration is closely approximated by the predictions based on the square-root law of the Gaussian model. When the mean count falls far below 1, however, the benefit of integration clearly exceeds the predictions based on the square-root law.
在双间隔强制选择检测任务的背景下，分析了来自独立泊松源（如神经元）信息的最优整合。当泊松分布的平均计数大于1时，整合的益处与基于高斯模型平方根定律的预测非常接近。然而，当平均计数远低于1时，整合的益处明显超过基于平方根定律的预测。
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