Robotic Double Patch Clamp Based on Interactive Mechanical Modeling for Functional Connectivity Measurement Between Neurons

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Abstract
Double patch clamp technique, using two micropipette electrodes to patch and measure electrophysiological signals of two neurons, is essential for investigating the functional connections between neurons in brain. However, the interactive mechanical disturbances from the dual-micropipette motions inside viscoelastic brain tissue cause dynamic drifts of neurons, making double patching low efficiency and challenging. In this paper, an interactive mechanical modeling of two micropipettes approaching two cells in elastic environment was established to estimate the dynamic drift of the cells. Based on that, a synchronous descent strategy, an appropriate relative position of two cells, and an online trajectory plan of two micropipettes were determined to improve double patch clamp efficiency. Finally, a robotic double patch clamp operation process was established for functional connectivity measurement between neurons in brain slice. The effectiveness of the proposed work is validated through both finite element modeling and experiments. The double patch clamp experiments on neurons in visual cortex demonstrate that our method achieves a 40% improvement in success rate and a 38% improvement in speed in comparison to the traditional manual method. With the above advantages, diverse functional connectivity activities between neurons were found using our system, paving a solid ground for further research.
Funding Information
  • National Natural Science Foundation of China (62273186)
  • Basic and Applied Basic Research Foundation of Guangdong Province (2024A1515011171)
  • Shenzhen Science and Technology Program (JCYJ20250604185817023)

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