Origin of information-limiting noise correlations

Significance Populations of neurons encode information in activity patterns that vary across repeated presentation of the same input and are correlated across neurons (noise correlations). Such noise correlations can limit information about sensory stimuli and therefore limit behavioral performance in tasks such as discrimination between two similar stimuli. Therefore it is important to understand where and how noise correlations are generated. Most previous accounts focused on sources of variability inside the brain. Here we focus instead on noise that is injected at the sensory periphery and propagated to the cortex: We show that this simple framework accounts for many known properties of noise correlations and explains behavioral performance in discrimination tasks, without the need to assume further sources of information loss. The ability to discriminate between similar sensory stimuli relies on the amount of information encoded in sensory neuronal populations. Such information can be substantially reduced by correlated trial-to-trial variability. Noise correlations have been measured across a wide range of areas in the brain, but their origin is still far from clear. Here we show analytically and with simulations that optimal computation on inputs with limited information creates patterns of noise correlations that account for a broad range of experimental observations while at same time causing information to saturate in large neural populations. With the example of a network of V1 neurons extracting orientation from a noisy image, we illustrate to our knowledge the first generative model of noise correlations that is consistent both with neurophysiology and with behavioral thresholds, without invoking suboptimal encoding or decoding or internal sources of variability such as stochastic network dynamics or cortical state fluctuations. We further show that when information is limited at the input, both suboptimal connectivity and internal fluctuations could similarly reduce the asymptotic information, but they have qualitatively different effects on correlations leading to specific experimental predictions. Our study indicates that noise at the sensory periphery could have a major effect on cortical representations in widely studied discrimination tasks. It also provides an analytical framework to understand the functional relevance of different sources of experimentally measured correlations.