Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Divisive normalization, a prominent model of interactions between neurons, plays a key role in influential theories of neural coding. Testing the mechanistic basis of those theories requires understanding the relationship between normalization and the statistics of neural activity, particularly noise correlations which provide strong constrains on circuit models. Yet the relation between normalization and correlations remains unclear, because models of covariability typically ignore normalization despite empirical evidence suggesting it contributes to stimulus and state modulations of covariability. We propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability, and accurately fits Ca2+ imaging data from mouse primary visual cortex (V1). We show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we outline the data regime in which the corresponding model parameter is identifiable. Our analysis provides the first quantitative evidence that similarly tuned neurons in mouse V1 share their normalization signals, and explains our observation that stronger normalization decorrelates those pairs, particularly at low stimulus contrast. Our model could enable quantifying the role of normalization on information transmission and representation, and mapping the parameters of normalization onto circuit and cellular mechanisms.