Learning to integrate parts for whole through correlated neural variability

Zhichao Zhu, Yang Qi, Wenlian Lu, Jianfeng Feng

Abstract: Author summary Understanding how the brain represents and processes perceptual information through neuronal firing patterns is at the heart of neuroscience. The prevailing idea suggests that the information is primarily encoded in mean firing rates, whereas correlations among neurons may play a secondary role. However, given that firing variability is ubiquitously observed in cortical neurons, one wonders if correlated noise may play a more central role in neural computation than previously thought. Here, we propose that perceptual information can be encoded in part or even entirely in the correlated variability of spiking neurons. Through a combination of theoretical modeling and machine learning approaches, we construct neural network models capable of processing correlated variability in a task-driven way. We demonstrate that the trained network is able to learn to extract covariance-encoded perceptual information to generate stimulus-selectivity in their mean firing rates, thanks to the nonlinear coupling of statistical moments of their activity. Information-theoretic analysis reveals a near-lossless transfer of perceptual information from the covariance of upstream neurons to the mean firing rate of downstream neurons. Our work offers new insights into the role of correlated variability in cortical processing and hints towards a task-driven paradigm for studying cortical computation with biologically plausible neural network models.