Probability density estimation for the interpretation of neural population codes
Probability density estimation for the interpretation of neural population codes
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DOI：
10.1152/jn.1996.76.4.2790
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发表时间：
1996-10-01
影响因子：
2.5
通讯作者：
Sanger, TD
中科院分区：
文献类型：
--
作者：
Sanger, TD
1. Georgopoulos及其同事首创的群体载体方法经常解释来自运动皮层和其他地方的多个细胞的电生理记录数据。本文提出了另一种方法来解释跨细胞群体的编码，这种方法可能在群体载体失败的情况下成功。在给定一组细胞的发射模式的情况下，利用概率论对种群编码进行分析，求出运动参数的完全条件概率密度。单个细胞放电时的条件概率密度与细胞对不同运动参数的放电速率调节曲线的形状成正比。当多个细胞发射时的条件密度与它们的调谐曲线的乘积成正比。运动参数可以用统计最大似然或最小均方误差方法从条件密度估计出来。仿真结果表明，密度估计可以正确地找到非均匀分布的首选方向和非余弦细胞调谐曲线的运动方向，而种群向量法在这些情况下失败。因此，概率方法为解释来自多个细胞的电生理记录数据提供了一种基于统计的替代种群载体。
1. Electrophysiological recording data from multiple cells in motor cortex and elsewhere often are interpreted using the population vector method pioneered by Georgopoulos and coworkers. This paper proposes an alternative method for interpreting coding across populations of cells that may succeed under circumstances in which the population vector fails.2. Population codes are analyzed using probability theory to find the complete conditional probability density of a movement parameter given the firing pattern of a set of cells.3. The conditional probability density when a single cell fires is proportional to the shape of the cell's tuning curve of firing rate in response to different movement parameters.4. The conditional density when multiple cells fire is proportional to the product of their tuning curves.5. Movement parameters can be estimated from the conditional density using statistical maximum likelihood or minimum mean-squared error methods.6. Simulations show that density estimation correctly finds movement directions for nonuniform distributions of preferred directions and noncosine cell tuning curves, whereas the population vector method fails for these cases.7. Probability methods thus provide a statistically based alternative to the population vector for interpreting electrophysiologic al recording data from multiple cells.