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Insects monitor the forces on their legs via sensory organs called campaniform sensilla (CS) that detect cuticular strain. The afferent signals from the CS produce highly dynamic, adaptive responses to even “simple” stimuli. To better understand the advantageous properties of the system, we constructed a dynamical model that describes some of these adaptive responses. We tuned the model parameters to reproduce the response time-courses from experimental data, and found that the model could describe a variety of additional responses with these same parameter values, suggesting that the model replicates the underlying dynamics of CS afferents without overfitting to the data. In addition, our model captures several gross characteristics of CS responses: 1) Responses encode the magnitude of the applied force; 2) The peak response reflects the rate at which the force is applied; 3) The response adapts to constant applied forces; and 4) The response shows hysteresis under cyclic loading. Improved replication of CS responses to applied forces will enable a more thorough understanding of how the nervous system detects forces and controls walking, and will lead to the development of more robust, self-calibrating strain sensors for robots.

We study leg joint torques of a large insect (Carausius morosus) to infer the functions of individual joints in closed kinematic chains during unrestrained walking. Leg joints were found to differentially contribute to multiple locomotor functions of a leg, such as body weight support and propulsion. We conclude that quantifying joint torques in freely behaving hexapods may provide a powerful tool in unraveling the feedback control strategies underlying motor flexibility.