Characterizing the cellular properties of neurons is fundamental to understanding their function in the brain. In this quest, the generation of bio-realistic models is central towards integrating multimodal cellular data sets and establishing causal relationships. However, current modeling approaches remain constrained by the limited availability and intrinsic variability of experimental neuronal data. The deterministic formalism of bio-realistic models currently precludes accounting for the natural variability observed experimentally. While deep learning is becoming increasingly relevant in this space, it fails to capture the full biophysical complexity of neurons, their nonlinear voltage dynamics, and variability. To address these shortcomings, we introduce NOBLE, a neural operator framework that learns a mapping from a continuous frequency-modulated embedding of interpretable neuron features to the somatic voltage response induced by current injection. Trained on synthetic data generated from bio-realistic neuron models, NOBLE predicts distributions of neural dynamics accounting for the intrinsic experimental variability. Unlike conventional bio-realistic neuron models, interpolating within the embedding space offers models whose dynamics are consistent with experimentally observed responses. NOBLE enables the efficient generation of synthetic neurons that closely resemble experimental data and exhibit trial-to-trial variability, offering a $4200\times$ speedup over the numerical solver. NOBLE is the first scaled-up deep learning framework that validates its generalization with real experimental data. To this end, NOBLE captures fundamental neural properties in a unique and emergent manner that opens the door to a better understanding of cellular composition and computations, neuromorphic architectures, large-scale brain circuits, and general neuroAI applications.

翻译：表征神经元的细胞特性是理解其在大脑中功能的基础。在这一探索中，生成生物逼真模型对于整合多模态细胞数据集和建立因果关系至关重要。然而，当前建模方法仍受限于实验神经元数据的有限可用性和内在变异性。生物逼真模型的确定性形式化目前无法解释实验中观察到的自然变异性。尽管深度学习在这一领域日益重要，但它未能完全捕捉神经元的完整生物物理复杂性、其非线性电压动态及变异性。为克服这些不足，我们提出了NOBLE，一种神经算子框架，它学习从可解释神经元特征的连续频率调制嵌入到由电流注入诱导的胞体电压响应的映射。通过在生物逼真神经元模型生成的合成数据上进行训练，NOBLE能够预测考虑内在实验变异性的神经动态分布。与传统的生物逼真神经元模型不同，在嵌入空间内插值可得到动态与实验观察响应一致的模型。NOBLE能够高效生成与实验数据高度相似且呈现试次间变异性的合成神经元，相比数值求解器实现了$4200\times$的加速。NOBLE是首个通过真实实验数据验证其泛化能力的大规模深度学习框架。由此，NOBLE以独特且涌现的方式捕捉了基本神经特性，为更好地理解细胞组成与计算、神经形态架构、大规模脑回路以及通用神经AI应用开辟了新途径。