With the recent advances in deep learning enabling rapid and accurate identification of complex structures, it’s application to in vitro patch-clamp electrophysiology was inevitable. We are exploring the application of these techniques to enhance the capabilities of our fully-automatic patch clamp robots. For example, we have demonstrated fully automatic, real-time detection of healthy neurons within traditional DIC images and vision-based techniques to correct for hardware error stack-up during pipette localization.
M.C. Yip, M.M. Gonzalez, C.R. Valenta, M.J.M. Rowan, C.R. Forest. Deep learning-based real-time detection of neurons in brain slices for in vitro physiology. Sci Rep 11, 6065 (2021). https://doi.org/10.1038/s41598-021-85695-4
M.M. Gonzalez, M.C. Yip, C.F. Lewallen, M.J. Rowan, C.R. Forest, Machine learning-based pipette correction for automated patch clamp in vitro, SfN Global Connectome, Virtual Conference, Jan 11-13, 2021.