Bush, N. E. | bioRxiv Science

bioRxiv2019

Continuous, multidimensional coding of 3D complex tactile stimuli by primary sensory neurons of the vibrissal system

To reveal the full representational capabilities of sensory neurons, it is essential to observe their responses to complex stimuli. In the rodent vibrissal system, mechanical information at the whisker base drives responses of primary sensory neurons in the trigeminal ganglion (Vg). Studies of how Vg neurons encode stimulus properties are typically limited to 2D analyses and restricted stimulus sets. Here we record from Vg neurons during 3D stimulation while quantifying the complete 3D whisker shape and mechanics. Results show that individual Vg neurons simultaneously represent multiple mechanical features of the stimulus, do not preferentially encode principal components of the stimuli, and represent continuous and tiled variations of all available mechanical information. As a population, the neurons span a continuum of rapid and slow adaptation properties; a binary distinction between these adaptation classes is oversimplified. These results contrast with proposed codes in which Vg neurons segregate into functional classes.

neuroscience↗

bioRxiv2019

WHISKiT Physics: A three-dimensional mechanical model of the rat vibrissal array

Rodents tactually explore the environment using ~62 whiskers (vibrissae), regularly arranged in arrays on both sides of the face. The rat vibrissal system is one of the most commonly used models to study how the brain encodes and processes somatosensory information. To date, however, researchers have been unable to quantify the mechanosensory input at the base of each whisker, because the field lacks accurate models of three-dimensional whisker dynamics. To close this gap, we developed WHISKiT Physics, a simulation framework that incorporates realistic morphology of the full rat whisker array to predict time-varying mechanical signals for all whiskers. The dynamics of single whiskers were optimized based on experimental data, and then validated against free tip oscillations and the dynamic response to collision. The model is then extrapolated to include all whiskers in the array, taking into account each whiskers individual geometry. Simulations of first mode resonances across the array approximately match previous experimental results and fall well within the range expected from biological variability. Finally, we use WHISKiT Physics to simulate mechanical signals across the array during three distinct behavioral conditions: passive whisker stimulation, active whisking against two pegs, and active whisking in a natural environment. The results demonstrate that the simulation system can be used to predict input signals during a variety of behaviors, something that would be difficult or impossible in the biological animal. In all behavioral conditions, interactions between array morphology and individual whisker geometry shape the tactile input to the whisker system.

neuroscience↗