Generation of biophysical neuron model parameters from recorded electrophysiological responses

Recent advances in connectomics, biophysics, and neuronal electrophysiology warrant modeling of neurons with further details in both network interaction and cellular dynamics. Such models may be referred to as ElectroPhysiome, as they incorporate the connectome and individual neuron electrophysiology to simulate neuronal activities. The nervous system of Caenorhabditis elegans is considered a viable framework for such ElectroPhysiome studies due to advances in connectomics of its somatic nervous system and electrophysiological recordings of neuron responses. In order to achieve a simulated ElectroPhysiome, the set of parameters involved in modeling individual neurons needs to be estimated from electrophysiological recordings. Here, we address this challenge by developing a deep generative estimation method called ElectroPhysiomeGAN (EP-GAN), which, once trained, can instantly generate parameters associated with the Hodgkin–Huxley neuron model (HH-model) for multiple neurons with graded potential response. The method combines generative adversarial network (GAN) architecture with recurrent neural network encoder and can generate an extensive number of parameters (>170) given the neuron’s membrane potential responses and steady-state current profiles. We validate our method by estimating HH-model parameters for 200 simulated neurons with graded membrane potential followed by nine experimentally recorded neurons (where six of them are newly recorded) in the nervous system of C. elegans . Comparison of EP-GAN with existing estimation methods shows EP-GAN's advantage in the accuracy of estimated parameters and inference speed for both small and large numbers of parameters being inferred. In addition, the architecture of EP-GAN permits input with arbitrary clamping protocols, allowing inference of parameters even when partial membrane potential and steady-state currents profiles are given as inputs. EP-GAN is designed to leverage the generative capability of GAN to align with the dynamical structure of the HH-model and thus is able to achieve such performance.