A framework for disentangling spatial and visual neural representations

Abstract: Neurons in cortical areas often integrate signals from different origins. In the primary visual cortex (V1), neural responses are modulated by non-visual context such as the animal's position. However, the spatial profile of these position signals across the environment remains unknown. Here, we propose a new framework to disentangle visual and spatial contributions in virtual reality. This method relies on two principles: 1) a virtual corridor design that decorrelates vision and space through targeted cue repetitions and manipulations and 2) a Generalized Linear Model (GLM) that explicitly estimates visual contributions in retinotopic rather than environmental coordinates. In simulations, we demonstrate that this framework is highly specific (recovering spatial modulation only when present) and effectively captures the profile and weight of spatial gain fields across the environment. When applied to V1 recordings from mice navigating the virtual corridor, the model isolated significant spatial components in a substantial fraction of V1 neurons. The recovered spatial components exhibited heterogeneous, often multi-peaked, profiles. Application of this framework to large-scale recordings may provide a robust approach to characterize the nature of spatial signals modulating sensory processing across brain areas.