The somatosensory system decodes a wide range of tactile stimuli and thus endows us with a remarkable capacity for object recognition, texture discrimination, sensory-motor feedback and social exchange. The first step leading to perception of innocuous touch is activation of cutaneous sensory neurons called low-threshold mechanoreceptors (LTMRs). Here, we review the properties and functions of LTMRs, emphasizing the unique tuning properties of LTMR subtypes and the organizational logic of their peripheral and central axonal projections. We discuss the spinal cord neurophysiological representation of complex mechanical forces acting upon the skin and current views of how tactile information is processed and conveyed from the spinal cord to the brain. An integrative model in which ensembles of impulses arising from physiologically distinct LTMRs are integrated and processed in somatotopically aligned mechanosensory columns of the spinal cord dorsal horn underlies the nervous system’s enormous capacity for perceiving the richness of the tactile world.

Summary Innocuous touch of the skin is detected by distinct populations of low-threshold mechanoreceptors (LTMRs), which are classified as Aβ-, Aδ- and C-LTMRs. Here, we report genetic labeling of LTMR subtypes and visualization of their relative patterns of axonal endings in hairy skin and the spinal cord. We found that each of the three major hair follicle types of trunk hairy skin; guard, awl/auchene, and zigzag hairs, is innervated by a unique and invariant combination of LTMRs; thus, each hair follicle type is a functionally distinct mechanosensory end organ. Moreover, the central projections of Aβ-, Aδ- and C-LTMRs that innervate the same or adjacent hair follicles form narrow LTMR columns in the dorsal horn. These findings support a model of mechanosensation in which the activities of Aβ-, Aδ- and C-LTMRs are integrated within dorsal horn LTMR columns and processed into outputs that underlie the perception of myriad touch sensations.

Summary Complex animal behaviors are likely built from simpler modules, but their systematic identification in mammals remains a significant challenge. Here we use depth imaging to show that three-dimensional (3D) mouse pose dynamics are structured at the sub-second timescale. Computational modeling of these fast dynamics effectively describes mouse behavior as a series of reused and stereotyped modules with defined transition probabilities. We demonstrate this combined 3D imaging and machine learning method can be used to unmask potential strategies employed by the brain to adapt to the environment, to capture both predicted and previously-hidden phenotypes caused by genetic or neural manipulations, and to systematically expose the global structure of behavior within an experiment. This work reveals that mouse body language is built from identifiable components and is organized in a predictable fashion; deciphering this language establishes an objective framework for characterizing the influence of environmental cues, genes and neural activity on behavior.