This project will detail the design of fast voltammetry waveforms for neurochemical detection using Bayesian optimization. Fast voltammetry is conventionally used to detect neurochemicals in the brain. However, the voltammetry waveform of choice is an underappreciated source of information content due to a lack of design principles and intractable design space. Here, we will use real-world in vitro data to create a training set to find optimal voltammetry waveforms for serotonin. We show how to use Scikit-Optimize in a simple, ask/tell fashion for experimentalists doing wet-lab or ‘human in the loop’ experiments.

References:

1. DOI 10.1007/s00216-021-03665-1