Digital twin reveals combinatorial code of non-linear computations in the mouse primary visual cortex

Abstract

More than a dozen excitatory cell types have been identified in the mouse primary visual cortex (V1) based on transcriptomic, morphological and in vitro electrophysiological features. However, the functional landscape of excitatory neurons with respect to their responses to visual stimuli is currently unknown. Here, we combined large-scale two-photon imaging and deep learning neural predictive models to study the functional organization of mouse V1 using digital twins. Digital twins enable exhaustive in silico functional characterization providing a bar code summarizing the input-output function of each neuron. Clustering the bar codes revealed a continuum of function with around 30 modes. Each mode represented a group of neurons that exhibited a specific combination of stimulus selectivity and nonlinear response properties such as cross-orientation inhibition, size-contrast tuning and surround suppression. These non-linear properties were expressed independently spanning all possible combinations across the population. This combinatorial code provides the first large-scale, data-driven characterization of the functional organization of V1. This powerful approach based on digital twins is applicable to other brain areas and to complex non-linear systems beyond the brain.