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

Code

• github.com/gmahuas/noisecorr
  OfficialIn paperpytorch★ 0

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, unless neurons are perfectly correlated by the stimulus. 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.

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