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Topography of distance-modulated multisensory object location encoding in mouse area RL

Zhang, Y.; Lewin, U.; La Chioma, A.; Bonhoeffer, T.; Huebener, M.

2026-02-06 neuroscience
10.64898/2026.02.04.701688 bioRxiv
Show abstract

The spatial arrangement of an animals sensory environment is encoded by spatial codes of the external world, often simultaneously in different modalities. How these modalities are brought together in a universal spatial map and how potential differences are reconciled during multisensory integration remains unclear. To tackle this question, we systematically mapped object location receptive fields (OLRFs) across different sensory modalities in area RL, a pivot of visuo-tactile integration within mouse posterior parietal cortex, using a custom high-resolution three-dimensional multisensory stimulation system. Most RL neurons have three-dimensional receptive fields, with many exhibiting OLRFs across visual, tactile (i.e. vibrissal), and bimodal stimulus conditions. Comparing OLRFs across modalities revealed that distance strongly shapes visuo-tactile integration, reflected in the shift of bimodal OLRFs from tactile-dominant to visual-dominant with increasing distance. Distance also affects the linearity and spatial mode of integration. At near distances, neurons integrate object location information sublinearly from overlapping regions of visual and tactile OLRFs. At far distances, information is integrated supralinearly from regions covered by OLRFs of either modality. This distance dependency extends to cortical organization, leading to a distance tuning gradient along which the spatial, modality, and integration properties of RL neurons are aligned. Together, our findings suggest distance as a key factor in visuo-tactile integration, reflecting that the limited reach of whiskers makes visual information more important for farther objects. In parallel, the system shifts from a localization dominated mode for nearby objects to a detection dominated mode for farther objects.

Published in Current Biology (predicted rank #4) · training set

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