Functional Architecture and Encoding of Tactile Sensorimotor Behavior in Rat Posterior Parietal Cortex

Abstract

The posterior parietal cortex (PPC) in rodents is reciprocally connected to primary somatosensory and vibrissal motor cortices. The PPC neuronal circuitry could thus encode and potentially integrate incoming somatosensory information and whisker motor output. However, the information encoded across PPC layers during refined sensorimotor behavior remains largely unknown. To uncover the sensorimotor features represented in PPC during voluntary whisking and object touch, we performed loose-patch single-unit recordings and extracellular recordings of ensemble activity, covering all layers of PPC in anesthetized and awake, behaving male rats. First, using single-cell receptive field mapping, we revealed the presence of coarse somatotopy along the mediolateral axis in PPC. Second, we found that spiking activity was modulated during exploratory whisking in layers 2–4 and layer 6, but not in layer 5 of awake, behaving rats. Population spiking activity preceded actual movement, and whisker trajectory endpoints could be decoded by population spiking, suggesting that PPC is involved in movement planning. Finally, population spiking activity further increased in response to active whisker touch but only in PPC layers 2–4. Thus, we find layer-specific processing, which emphasizes the computational role of PPC during whisker sensorimotor behavior.

SIGNIFICANCE STATEMENT The posterior parietal cortex (PPC) is thought to merge information on motor output and sensory input to orchestrate interaction with the environment, but the function of different PPC microcircuit components is poorly understood. We recorded neuronal activity in rat PPC during sensorimotor behavior involving motor and sensory pathways. We uncovered that PPC layers have dedicated function: motor and sensory information is merged in layers 2–4; layer 6 predominantly represents motor information. Collectively, PPC activity predicts future motor output, thus entailing a motor plan. Our results are important for understanding how PPC computationally processes motor output and sensory input. This understanding may facilitate decoding of brain activity when using brain–machine interfaces to overcome loss of function after, for instance, spinal cord injury.