Accurately fitting biophysical neuron models to experimental voltage data enabled by meta-learning.

The neuron is a fundamental unit of computation in the brain. Neuronal firing properties are governed by ion channels distributed across complex morphologies. Determining ionic conductances from experimental observations of intracellular membrane potentials would advance basic understanding and clinical translation, but is currently considered intractable. To enable accurate fitting of biophysical neuron models to experimental somatic voltage recordings, we developed a meta-learning algorithm ('CoMParE'). Using CoMParE to fit standard biophysically detailed models, we demonstrate state-of-the-art reproduction of experimental data and quantify parameter estimate precision and accuracy. We additionally fit models with enhanced electrophysiological detail and demonstrate further improvements in experimental data reproduction. Finally, we show that these improvements result from convexifying the objective function loss surface through meta-learning. These results demonstrate that highly detailed biophysical models can accurately reproduce experimental data, enabling determination of ionic conductances underlying neuronal function and neurological disorders, and indicating that this inverse problem is tractable.