A dynamical model for generating synthetic data to quantify active tactile sensing behavior in the rat.

Area of Science:

• Neuroscience
• Biophysics
• Robotics

Background:

• Modeling complex neural networks requires understanding actively acquired sensory information.
• The biomechanics of sensory acquisition directly influence neural processing.
• Rodent vibrissal systems offer a tractable model for closed-loop sensorimotor processing.

Purpose of the Study:

• To present a simulation framework, WHISKiT Physics, for modeling sensory signals acquired by the rodent vibrissal array.
• To predict time-varying mechanical signals generated at the base of each whisker during active environmental sampling.
• To explore the impact of whisker array morphology and individual whisker dynamics on neural input signals.

Main Methods:

• Developed a simulation framework, WHISKiT Physics, incorporating realistic rat whisker array morphology.
• Optimized single-whisker dynamics based on experimental data.
• Validated the model against free tip oscillations and collision responses, then extrapolated to the full array.

Main Results:

• WHISKiT Physics successfully simulates signals acquired by the full vibrissal array during active sampling.
• Simulations demonstrate that active whisking enhances in-plane whisker bending compared to passive stimulation.
• Principal component analysis reveals contributions of whisker identity and mechanics to vibrissotactile responses.

Conclusions:

• WHISKiT Physics enables simulation of sensory input signals during behaviors not feasible in live animals.
• Interactions between whisker array morphology and individual whisker geometry shape the signals processed by the brain.
• The model provides a powerful tool for studying sensorimotor processing in rodents.