In vivo closed-loop control of a locust's leg using nerve stimulation

Abstract

Activity of an innervated tissue can be modulated based on an acquired biomarker through feedback loops. How to convert this biomarker into a meaningful stimulation pattern is still a topic of intensive research. In this article, we present a simple closed-loop mechanism to control the mean angle of a locust's leg in real time by modulating the frequency of the stimulation on its extensor motor nerve. The nerve is interfaced with a custom-designed cuff electrode and the feedback loop is implemented online with a proportional control algorithm, which runs solely on a microcontroller without the need of an external computer. The results show that the system can be controlled with a single-input, single-output feedback loop. The model described in this article can serve as a primer for young researchers to learn about neural control in biological systems before applying these concepts in advanced systems. We expect that the approach can be advanced to achieve control over more complex movements by increasing the number of recorded biomarkers and selective stimulation units.

Figure 1

Scheme of the implemented control loop. The angle set point is selected and the measured leg angle subtracted from it to conform the error signal, which is sampled by the microcontroller. A proportional control algorithm runs in the microcontroller, varying the period of the stimulus signal applied on the nerve. The leg angle is sensed with a flexible resistor to provide the feedback.

Figure 2

PDMS cuff fabrication steps. (a) The mold for the cuff is 3D printed as two separated parts. After assembly, the two parts form a cavity with the desired dimension of the cuff. (b) The molds are tightly screwed together, and a needle is placed inside the mold to form the cuff’s lumen. PDMS is cast inside the resulting tube-shaped cavity. (c) The excess air in the PDMS is removed by degassing in a vacuum chamber and the PDMS is thermally cured. (d) After curing, the tube is detached from the mold and the stimulation electrodes are inserted through the PDMS tube. The cuff is then cut to a length of ~ 1 mm and opened along the tube to enable nerve insertion. Finally, the exterior of the electrode is coated with parylene-C.

Figure 3

(a) Locusta migratoria prior to the surgery in clay bed. (b) Scheme of the metathoracic ganglion and the nerves innervating the hind leg. (c) Microscope image of the surgical incision on the metathorax of the locust, showcasing the N5 immediately before insertion in the cuff electrode.

Figure 4

Electrochemical characterization of the custom-made cuff electrodes in saline solution. The characterization was performed in a bipolar configuration between the two stimulation electrodes in the cuff. (a) Chronoamperogram for three different voltage step amplitudes. (b) Impedance spectroscopy analysis exhibiting a combined impedance of ~ 1.4 kΩ @ 1 kHz.

Figure 5

A set point voltage is applied to determine the desired angle of the leg. The error between set point and feedback signals is converted into a stimulation pulse pattern (red), and a corresponding inter-pulse delay. The stimulation pattern triggers a response in the leg angle, which is extracted from video recordings. The black traces show 10 repetitions of the same experiment with the thicker black traces corresponding to the indicated pulse pattern.

Figure 6

A cyclic voltage ramp set point is applied to generate movement patterns of a locust’s leg. The error signal between set point and feedback signals is converted into a stimulation pulse pattern (red), and a corresponding inter-pulse delay. The stimulation pattern triggers a response in the leg angle, which is extracted from video recordings. The black traces show 5 repetitions of the same experiment with the thicker black traces corresponding to the indicated pulse pattern.

Figure 7

Scatter plot of the angle of the leg versus the frequency of stimulation, computed as the inverse of the inter-pulse delay, for the voltage ramp experiment. The upper cluster (red) represents the trajectory of flexion of the leg, whereas the lower cluster (black) represents the trajectory of extension of the leg. The linear fits are computed using the method of least-squares.