Interpreting neuronal population activity by reconstruction: Unified framework with application to hippocampal place cells
Interpreting neuronal population activity by reconstruction: Unified framework with application to hippocampal place cells
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DOI：
10.1152/jn.1998.79.2.1017
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发表时间：
1998-02-01
影响因子：
2.5
通讯作者：
Sejnowski, TJ
中科院分区：
文献类型：
--
作者：
Zhang, KC;Ginzburg, I;Sejnowski, TJ
物理变量，如视野中线条的方向或身体在空间中的位置，被编码为神经元群体的活动水平。重建或解码是一个逆问题，其中物理变量是从观察到的神经活动估计。重建是有用的，首先在量化多少信息的物理变量是目前在人口中，第二，在提供洞察大脑如何可能使用分布式表示在解决相关的计算问题，如视觉对象识别和空间导航。讨论了两类重建方法，即概率或贝叶斯方法和基函数方法。它们包括重要的现有方法作为特殊情况，如人口矢量编码，最优线性估计和模板匹配。作为重建问题的一个代表性例子，不同的方法被施加到自由移动大鼠海马位置细胞的多电极锋电位序列数据。比较了不同方法对大鼠运动轨迹的重建精度。贝叶斯方法是特别准确的连续性约束时，强制执行，最好的错误是在一个因素的两个信息理论的限制如何准确的任何重建可以是和位置跟踪的内在实验误差相媲美。此外，重建分析揭示了位置细胞活动的一些有趣方面，例如当动物停止奔跑时，重建轨迹的不稳定跳跃趋势。通常，最小可实现重建误差的理论值量化了在均方误差的意义上物理变量在神经元群体中编码的准确程度，而不管用于阅读信息的方法如何。一个相关的结果是，理论精度是独立的高斯调谐函数的宽度只有在两个维度。最后，本文所考虑的所有重建方法都可以通过一个统一的神经网络架构来实现，大脑可以切实地使用它来解决相关问题。
Physical variables such as the orientation of a line in the visual field or the location of the body in space are coded as activity levels in populations of neurons. Reconstruction or decoding is an inverse problem in which the physical variables are estimated from observed neural activity. Reconstruction is useful first in quantifying how much information about the physical variables is present in the population and, second, in providing insight into how the brain might use distributed representations in solving related computational problems such as visual object recognition and spatial navigation. Two classes of reconstruction methods, namely, probabilistic or Bayesian methods and basis function methods, are discussed. They include important existing methods as special cases, such as population vector coding, optimal linear estimation, and template matching. As a representative example for the reconstruction problem, different methods were applied to multielectrode spike train data from hippocampal place cells in freely moving rats. The reconstruction accuracy of the trajectories of the rats was compared for the different methods. Bayesian methods were especially accurate when a continuity constraint was enforced, and the best errors were within a factor of two of the information-theoretic limit on how accurate any reconstruction can be and were comparable with the intrinsic experimental errors in position tracking. In addition, the reconstruction analysis uncovered some interesting aspects of place cell activity, such as the tendency for erratic jumps of the reconstructed trajectory when the animal stopped running. In general, the theoretical values of the minimal achievable reconstruction errors quantify how accurately a physical variable is encoded in the neuronal population in the sense of mean square error, regardless of the method used for reading out the information. One related result is that the theoretical accuracy is independent of the width of the Gaussian tuning function only in two dimensions. Finally, all the reconstruction methods considered in this paper can be implemented by a unified neural network architecture, which the brain feasibly could use to solve related problems.