Distinct cortical spatial representations learned along disparate visual pathways

Recent experimental studies have found diverse spatial properties, such as head direction tuning and egocentric tuning, of neurons in the postrhinal cortex (POR) and revealed how the POR spatial representations are distinct from the retrosplenial cortex (RSC). However, how these spatial properties of POR neurons emerge is unknown, and the cause of distinct cortical spatial representations is also unclear. We have previously modeled spatial tuning in RSC based on processing of static visual features originating from the LGN-V1 pathway. However, recent studies have indicated that visual inputs to POR primarily reflect motion processing originating in the superior colliculus (SC). Here, we build a new learning model based on the computation of optic flow information in the SC. This SC-based optic flow model produces simulated neurons with spatial tuning that reflects the diverse spatial properties of POR neurons. Moreover, comparing the new optic flow model with our previously proposed static feature model, we show that distinct cortical spatial representations similar to those found in POR and RSC can be learned along disparate visual pathways (originating in SC and V1), suggesting that the varying features encoded in different visual pathways contribute to the distinct spatial properties in downstream cortical areas.