论文标题
一个小相关扩展，以量化嘈杂的感觉系统中的信息
A small-correlation expansion to quantify information in noisy sensory systems
论文作者
论文摘要
神经网络通过响应外部刺激来编码信息。这种种群反应嘈杂且密切相关，刺激引起的相关性与共享噪声引起的相关性之间存在复杂的相互作用。迄今为止，了解这些相关性如何影响信息传播已限于对或小组神经元，因为维度的诅咒阻碍了对较大人群中相互信息的评估。在这里，我们开发了一个小相关的扩展，以计算大量神经元携带的刺激信息，从而根据神经元的发射速率和成对的相关性产生可解释的分析表达式。我们验证了综合数据的近似值，并证明了其适用于脊椎动物视网膜中电生理记录的适用性，从而使我们能够量化神经元与单个神经元中神经元和记忆之间的噪声相关性的影响。
Neural networks encode information through their collective spiking activity in response to external stimuli. This population response is noisy and strongly correlated, with complex interplay between correlations induced by the stimulus, and correlations caused by shared noise. Understanding how these correlations affect information transmission has so far been limited to pairs or small groups of neurons, because the curse of dimensionality impedes the evaluation of mutual information in larger populations. Here we develop a small-correlation expansion to compute the stimulus information carried by a large population of neurons, yielding interpretable analytical expressions in terms of the neurons' firing rates and pairwise correlations. We validate the approximation on synthetic data and demonstrate its applicability to electrophysiological recordings in the vertebrate retina, allowing us to quantify the effects of noise correlations between neurons and of memory in single neurons.
