Parameter Extraction from Population Codes: A Critical Assessment
Parameter Extraction from Population Codes: A Critical Assessment
复制标题
DOI：
10.1162/neco.1996.8.3.511
复制
发表时间：
1996-04
影响因子：
2.9
通讯作者：
H. Snippe
中科院分区：
文献类型：
--
作者：
H. Snippe
在感知系统中，可以通过确定一组神经传感器的响应轮廓的重心来提取刺激参数。同样，在神经系统的运动端，重心解码，也称为矢量解码，根据神经激活轮廓生成运动方向。我们从统计的角度对这些方案进行评估，将它们的统计方差与从噪声神经元集合激活轮廓中无偏参数提取所可能的最小方差进行比较。重心译码可以是统计上最优的。这是具有由泊松统计描述的输出的具有高斯调谐分布的常规传感器阵列的情况，以及对于具有用于估计的(角度)参数的正弦调谐分布的传感器阵列的情况。然而，在许多情况下，重心解码的效率非常低。这包括传感器位置非常不规则的重要情况。最后，我们研究了重心译码在信息处理层次的不同阶段对响应非线性的鲁棒性。我们的结论是，在神经系统中，与其显式地表示参数，不如将参数隐含地编码在神经元集合激活轮廓中，这是更安全的。
In perceptual systems, a stimulus parameter can be extracted by determining the center-of-gravity of the response profile of a population of neural sensors. Likewise at the motor end of a neural system, center-of-gravity decoding, also known as vector decoding, generates a movement direction from the neural activation profile. We evaluate these schemes from a statistical perspective, by comparing their statistical variance with the minimum variance possible for an unbiased parameter extraction from the noisy neuronal ensemble activation profile. Center-of-gravity decoding can be statistically optimal. This is the case for regular arrays of sensors with gaussian tuning profiles that have an output described by Poisson statistics, and for arrays of sensors with a sinusoidal tuning profile for the (angular) parameter estimated. However, there are also many cases in which center-of-gravity decoding is highly inefficient. This includes the important case where sensor positions are very irregular. Finally, we study the robustness of center-of-gravity decoding against response nonlinearities at different stages of an information processing hierarchy. We conclude that, in neural systems, instead of representing a parameter explicitly, it is safer to leave the parameter coded implicitly in a neuronal ensemble activation profile.