Centre for Integrative Neuroscience and Institute for Theoretical Physics, University of Tübingen, Tübingen, Germany.
J Neurosci. 2011 Oct 5;31(40):14272-83. doi: 10.1523/JNEUROSCI.2539-11.2011.
The amount of information encoded by networks of neurons critically depends on the correlation structure of their activity. Neurons with similar stimulus preferences tend to have higher noise correlations than others. In homogeneous populations of neurons, this limited range correlation structure is highly detrimental to the accuracy of a population code. Therefore, reduced spike count correlations under attention, after adaptation, or after learning have been interpreted as evidence for a more efficient population code. Here, we analyze the role of limited range correlations in more realistic, heterogeneous population models. We use Fisher information and maximum-likelihood decoding to show that reduced correlations do not necessarily improve encoding accuracy. In fact, in populations with more than a few hundred neurons, increasing the level of limited range correlations can substantially improve encoding accuracy. We found that this improvement results from a decrease in noise entropy that is associated with increasing correlations if the marginal distributions are unchanged. Surprisingly, for constant noise entropy and in the limit of large populations, the encoding accuracy is independent of both structure and magnitude of noise correlations.
神经元网络所编码的信息量极大程度上取决于其活动的相关结构。具有相似刺激偏好的神经元之间的噪声相关性往往高于其他神经元。在同质神经元群体中，这种有限范围的相关结构对群体编码的准确性极为不利。因此，注意力、适应或学习后的尖峰计数相关性降低被解释为更有效的群体编码的证据。在这里，我们在更现实、更异质的群体模型中分析了有限范围相关性的作用。我们使用 Fisher 信息和最大似然解码来表明，相关性降低并不一定能提高编码准确性。事实上，在具有几百个以上神经元的群体中，增加有限范围相关性的水平可以显著提高编码准确性。我们发现，这种改进是由于噪声熵的降低所致，如果边际分布不变，则与相关性的增加相关。令人惊讶的是，对于恒定的噪声熵和在大群体的极限情况下，编码准确性与噪声相关性的结构和幅度都无关。
