
ElectroPhysiomeGAN은 C. elegans 뉴런의 막전위 반응과 정상상태 전류 프로파일로부터 Hodgkin-Huxley 뉴런 모델의 매개변수를 생성할 수 있는 새로운 심층 생성 모델이다.


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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 C. 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 need to be estimated from electrophysiological recordings. Here, we address this challenge by developing a novel deep generative method called ElectroPhysiomeGAN (EP-GAN), which once trained, can instantly generate parameters associated with the Hodgkin-Huxley neuron model (HH-model) for neurons with graded potential response. The method combines Generative Adversarial Network (GAN) architecture with Recurrent Neural Network (RNN) 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 synthetic neurons with graded membrane potential followed by 9 experimentally recorded neurons (where 6 of them newly recorded) in the nervous system of C. elegans . Compared to other methods, EP-GAN is advantageous in both accuracy of generated parameters and inference speed. In addition, EP-GAN preserves performance when provided with incomplete membrane potential responses up to 25% and steady-state current profiles up to 75%. EP-GAN is designed to leverage the generative capability of GAN to align with the dynamical structure of HH-model, and thus able to achieve such performance.

### Competing Interest Statement

The authors have declared no competing interest.

ElectroPhysiomeGAN: Generation of Biophysical Neuron Model Parameters from Recorded Electrophysiological Responses

c-elegans-신경-모델-매개변수-예측을-위한-electrophysiomegan-기록된-전기생리학적-반응으로부터-생물물리학적-뉴런-모델-매개변수-생성


C. elegans 신경 모델 매개변수 예측을 위한 ElectroPhysiomeGAN: 기록된 전기생리학적 반응으로부터 생물물리학적 뉴런 모델 매개변수 생성
