Peri-Head Distance Coding in the Mouse Brainstem
Xiao, W.; Severson, K. S.; Zheng, H.; Chen, K.; Thompson, P. M.; Levy, M. S.; Choi, S.; Zhao, S.; Takatoh, J.; Prevosto, V.; Wang, F.
Show abstract
Perceiving object distance in peri-personal space is essential for guiding movement and avoiding danger. During active sensation, distance information is often anchored to the body via touch; yet how early somatosensory circuits extract distance information from tactile inputs remains unclear. Here, we investigate how second-order neurons in the mouse whisker brainstem encode peri-head distance. Using in vivo extracellular recordings in awake mice in a naturalistic wall-passing paradigm, we find brainstem neurons employ two distance-coding schemes: a "proximity" code, where firing increases monotonically as objects approach the face; and a "map" code, where neurons exhibit peak tuning at specific distances to collectively tile peri-head space. The map code outperforms proximity code in population decoding of distance. Perturbation experiments reveal multi-whisker integration and internuclear inhibition contribute to the generation of map-like tuning. These findings highlight a previously underappreciated computational role for brainstem circuits, where inhibition acts as a neural comparator to transform proximity-based sensory inputs into a map-like representation of peri-personal space.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- The sensorimotor strategies and neuronal representations of tactile shape discrimination in mice 97%
- Rapid suppression and sustained activation of distinct cortical regions for a delayed sensory-triggered motor response 97%
- Visual intracortical and transthalamic pathways carry distinct information to cortical areas 97%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.