Action-dependent state prediction in mouse posterior parietal cortex

Model-based decision making requires prediction of future states by action-dependent state transition models. To investigate their neural implementation, mice were trained to do an auditory virtual navigation task and neuronal activity was recorded in the posterior parietal cortex (PPC) and the posteromedial cortex (PM), with the genetically encoded calcium indicator GCaMP6f after gene transfer by AAV2/1 and 2-photon microscopy. A mouse was head restrained and maneuvered a spherical treadmill. 12 speakers around the treadmill provided an auditory virtual environment. The direction and amplitude of sound pulses emulated the location of the sound source, which was moved according to the mouse´s locomotion on the treadmill. When the mouse reached the sound source and licked a spout, it got a water reward. The task consisted of two conditions: continuous condition in which the guiding sound was presented continuously and intermittent condition in which the sound was presented intermittently. In both conditions, mice increased lickings as they approached the sound source, indicating that mice recognized the sound-source position and predicted a reward. In intermittent condition, the anticipatory licking was increased even when the sound was omitted, suggesting that mice maintained the predicted sound-source position based on their own actions. We optically recorded calcium transients of up to 500 neurons simultaneously in each of layers 2, 3 and 5 in 8 mice. A subset of neurons increased the activities as mice approached the goal. This increase of activities was observed both with and without sound inputs in all the layers of PPC, while the increase was observed only during sound inputs in PM. To test how the activities in PPC and PM contributed to the prediction of goal distance, we conducted a decoding analysis. Probabilistic decoder predicted the goal distance from the recorded population activities: the decoder was trained with the data in continuous condition. In PPC, the predicted distance significantly decreased both with and without sound inputs consistently with the actual distance to the goal. These results suggest that PPC realizes action-dependent state prediction in the absence of sensory input.