Strong, but not weak, noise correlations are beneficial for population coding

Abstract Neural correlations play a critical role in sensory information coding. They are of two kinds: signal correlations, when neurons have overlapping sensitivities, and noise correlations from network effects and shared noise. It is commonly thought that stimulus and noise correlations should have opposite signs to improve coding. However, experiments from early sensory systems and cortex typically show the opposite effect, with many pairs of neurons showing both types of correlations to be positive and large. Here, we develop a theory of information coding by correlated neurons which resolves this paradox. We show that noise correlations are always beneficial if they are strong enough. Extensive tests on retinal recordings under different visual stimuli confirm our predictions. Finally, using neuronal recordings and modeling, we show that for high dimensional stimuli noise correlation benefits the encoding of fine-grained details of visual stimuli, at the expense of large-scale features, which are already well encoded. Competing Interest Statement The authors have declared no competing interest. Data availibility Part of the data utilized in this work have been published in previous studies. The remaining data and codes will be shared upon publication of this study. The code used to generate the synthetic data is available at https://github.com/gmahuas/noisecorr.