Population coding in neuronal systems with correlated noise
Population coding in neuronal systems with correlated noise
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DOI：
10.1103/physreve.64.051904
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发表时间：
2001-11-01
影响因子：
2.4
通讯作者：
Shamir, M
中科院分区：
文献类型：
--
作者：
Sompolinsky, H;Yoon, H;Shamir, M
外部事件的神经元表征通常分布在大量细胞中。我们研究了相关噪声对这些神经元群体代码准确性的影响。我们的主要问题是，在相关噪声的存在下，是否可以通过增加人口N的大小来抑制人口代码中的固有错误。我们解决这个问题，使用一个模型的人口的神经元，广泛调整到一个角度变量在两个维度。神经元活动的波动被建模为具有成对相关性的高斯噪声，该高斯噪声随着相关细胞的优选角度之间的差异而指数衰减。我们假设系统是宽调谐的，这意味着相关长度和平均响应的调谐曲线的宽度跨越整个系统长度的相当大的一部分。系统的性能由Fisher信息（FI）来衡量，它限制了系统的估计误差。通过计算一个大的N的极限的FI，我们表明，正相关性降低了网络的估计能力，相对于不相关的人口。随着种群中细胞数量的增加，信息容量饱和到一个有限值。相反，负相关大大增加了神经元群体的信息容量。这些结果是补充的影响相关性的互信息的系统。我们的分析提供了一个估计的统计独立的自由度的有效数量，表示N-eff，一个大的相关系统可以有。根据我们的理论，N-eff在大N的极限下保持有限。从编码角度的某些皮层区域的实验数据中估计相关性和调谐曲线的参数，我们预测嵌入这些区域的局部群体中的有效自由度的数量小于或近似于10（2）。
Neuronal representations of external events are often distributed across large populations of cells. We study the effect of correlated noise on the accuracy of these neuronal population codes. Our main question is whether the inherent error in the population code can be suppressed by increasing the size of the population N in the presence of correlated noise. We address this issue using a model of a population of neurons that are broadly tuned to an angular variable in two dimensions. The fluctuations in the neuronal activities are modeled as Gaussian noises with pairwise correlations that decay exponentially with the difference between the preferred angles of the correlated cells. We assume that the system is broadly tuned, which means that both the correlation length and the width of the tuning curves of the mean responses span a substantial fraction of the entire system length. The performance of the system is measured by the Fisher information (FI), which bounds its estimation error. By calculating the FI in the limit of a large N, we show that positive correlations decrease the estimation capability of the network, relative to the uncorrelated population. The information capacity saturates to a finite value as the number of cells in the population grows. In contrast, negative correlations substantially increase the information capacity of the neuronal population. These results are supplemented by the effect of correlations on the mutual information of the system. Our analysis provides an estimate of the effective number of statistically independent degrees of freedom, denoted N-eff, that a large correlated system can have. According to our theory N-eff remains finite in the limit of a large N. Estimating the parameters of the correlations and tuning curves from experimental data in some cortical areas that code for angles, we predict that the number of effective degrees of freedom embedded in localized populations in these areas is less than or of the order of approximate to 10(2).