Biology subjects

Hartmann, M. J.

Publications and source records attributed to Hartmann, M. J..

3 recordsLinked to original sources

bioRxiv2025

Biomechanical simplification of the motor control of whisking

1.Animal nervous systems must coordinate the sequence and timing of numerous muscles - a challenging control problem. The challenge is particularly acute for highly mobile sensing structures with many degrees of freedom, such as eyes, pinnae, hands, forepaws, and whiskers, because these low-mass, distal sensors require complex muscle coordination. This work examines how the geometry of the rat whisker array simplifies coordination required for "whisking" behavior 1-3. During whisking, 33 intrinsic ("sling") muscles are the primary drivers 4-12 of the rapid, rhythmic protractions of the large mystacial vibrissae (whiskers), which vary more than sixfold in length and threefold in base diameter 13-16. Although whisking is a rhythmic, centrally-patterned behavior 17-24, rodents can change the position, shape, and size of the whisker array, indicating considerable voluntary control 25-34. To begin quantifying how the arrays biomechanics contribute to whisking movements, we used three-dimensional anatomical reconstructions of follicle and sling muscle geometry to simulate the movement resulting from a "uniform motor command," defined as equal firing rates across all sling muscle motor neurons. This simulation provides a baseline profile of protraction under anatomically realistic but uniformly driven conditions. It does not isolate neural from biomechanical contributions but helps identify deviations that suggest active control. Simulations reveal that all follicles rotate through approximately equal angles, regardless of size. The angular fanning of the whiskers at their bases increases monotonically throughout protraction, while maximum distance between whisker tips occurs at approximately 90% of resting muscle length, after which whisker tips converge and sensing resolution increases monotonically.

neuroscience↗

bioRxiv2022

Statistical characterization of tactile scenes in three-dimensional environments reveals filter properties of somatosensory cortical neurons

Natural scenes statistics have been studied extensively using collections of natural images and sound recordings. These studies have yielded important insights about how the brain might exploit regularities and redundancies in visual and auditory stimuli. In contrast, natural scenes for somatosensation have remained largely unexplored. Here we use three-dimensional scans of natural and human-made objects to quantify natural scene statistics at the scale of the human fingertip. Using measurements of distance, slope, and curvature from the object surfaces, we show that the first order statistics follow similar trends as have been observed for images of natural and human-made environments. In addition, independent component analysis of curvature measurements reveals Gabor-like basis vectors similar to those found in natural images. A simple neural model using these filters showed responses that accurately capture the statistics of responses in primate primary somatosensory cortex.

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↗