During active tactile exploration, the dynamic patterns of touch are transduced to electrical signals and transformed by the brain into a mental representation of the object under investigation. This transformation from sensation to perception is thought to be a major function of the mammalian cortex. In primary somatosensory cortex (S1) of mice, layer 5 (L5) pyramidal neurons are major outputs to downstream areas that influence perception, decision-making, and motor control. We investigated self-motion and touch representations in layer 5 of S1 with juxtacellular loose-seal patch recordings of optogenetically identified excitatory neurons. We found that during rhythmic whisker movement, 66% of neurons represent self-motion. This population was significantly more modulated by whisker angle than by phase. Upon active touch, a distinct pattern of activity was evoked across L5, which represented the whisker angle at the time of touch. Object location was decodable with submillimeter precision from the touch-evoked spike counts of a randomly sampled handful of these neurons. These representations of whisker angle during self-motion and touch were independent, both in the selection of which neurons were active, and in the angle-tuning preference of co-active neurons. Thus, the output of S1 transiently shifts from a representation of self-motion to an independent representation of explored object location during active touch.
Mice are trained to locate a pole across various locations (A). During the task, whisker motion (B and C) are captured along with targeted neural recordings to L5 (D) of primary somatosensory cortex.
Single example trial lasting 4 seconds. Example video (left) along with whisker traces, decomposed components, and spikes recorded from L5 (right)
Packaged under ./matlab are scripts used to characterize encoding and quantify touch location decoding using single neurons. For a summary of all results and findings using this script see Cheung et al., 2020. For a complete description of the behavioral paradigm during neural recordings refer to Cheung et al., 2019.
- All code is built and tested on MATLAB 2018b.
- glmnet from Qian and Hastie 2013 - https://web.stanford.edu/~hastie/glmnet_matlab/
- Complete dataset is available for download here! Email jacheung@usc.edu or shires@usc.edu for assistance in using the data.