SUMMARY

Understanding how the neurons of the brain communicate, connect, and respond to stimuli is a fundamental goal of neuroscience. Whole-cell patch clamp recording in vitro represents the gold standard method for measuring electrophysiology, morphology, and connectivity properties of single neurons—an ideal method for classifying neuronal cell types. Furthermore, the high spatiotemporal resolution provided by whole-cell patch clamping is particularly helpful in engineering better pharmacological, optogenetic, and chemigenetic effectors which can help lead to better tools to treat neural diseases and study the brain. However, the manual, laborious, and time-consuming nature of patch clamping experiments have limited the throughput and number of cells that can be sampled per day. To improve the throughput for these single cell, high spatiotemporal experiments, this work will focus on developing automated, robotic methods for cell-specific patch clamping to enable rapid characterization of cells to study their electrophysiological response to effectors and local synaptic connectivity. Towards this goal, I propose to (1) integrate automated patch clamping with discovery experiments for cellular indicators and effectors, (2) develop a machine learning algorithm for real-time neuron detection of neurons in brain slices for in vitro patch clamping, and (3) create a coordinated multi-pipette patch clamp algorithm for enabling high throughput synaptic connectivity studies. The development of these technologies will create a system that allows for high-efficiency experiments that yield multimodal cellular electrophysiology.