Inferring the biophysical parameters of conductance-based models (CBMs) from experimentally accessible recordings remains a central challenge in computational neuroscience. Spike times are the most widely available data, yet they reveal little about which combinations of ion channel conductances generate the observed activity. This inverse problem is further complicated by neuronal degeneracy, where multiple distinct conductance sets yield similar spiking patterns. We introduce a method that addresses this challenge by combining deep learning with Dynamic Input Conductances (DICs), a theoretical framework that reduces complex CBMs to three interpretable feedback components governing excitability and firing patterns. Our approach first maps spike times to DIC densities at threshold using a neural network that learns a low-dimensional representation of neuronal activity. The predicted DIC values are then used to generate degenerate CBM populations via an iterative compensation algorithm, ensuring compatibility with the intermediate target DICs, and thereby reproducing the corresponding firing patterns, even in high-dimensional models. Applied to two models, this algorithmic pipeline reconstructs spiking and bursting regimes with high accuracy and robustness to variability, including spike trains generated under noisy current injection mimicking physiological stochasticity. It produces diverse degenerate populations within milliseconds on standard hardware, enabling scalable and efficient inference from spike recordings alone. Together, this work positions DICs as a practical and interpretable link between experimentally observed activity and mechanistic models. By enabling fast and scalable reconstruction of degenerate populations directly from spike times, our approach provides a powerful way to investigate how neurons exploit conductance variability to achieve reliable computation.

翻译：从实验可获取的记录中推断电导模型（CBMs）的生物物理参数，仍然是计算神经科学的核心挑战。尖峰时间是最广泛可用的数据，但它们几乎无法揭示哪些离子通道电导组合产生了观测到的活动。神经元退化性进一步加剧了这一逆问题的复杂性，即多种不同的电导集合可产生相似的尖峰发放模式。我们提出一种方法，通过将深度学习与动态输入电导（DICs）相结合来应对这一挑战。DICs是一种理论框架，可将复杂的CBMs简化为控制兴奋性和发放模式的三个可解释的反馈分量。我们的方法首先使用神经网络将尖峰时间映射到阈值处的DIC密度，该网络学习神经元活动的低维表示。然后，预测的DIC值被用于通过迭代补偿算法生成退化的CBM种群，确保与中间目标DICs的兼容性，从而重现相应的发放模式，即使在高维模型中也是如此。应用于两个模型时，该算法流程能够高精度、高鲁棒性地重建尖峰发放和簇发放机制，包括在模拟生理随机性的噪声电流注入下产生的尖峰序列。它能在标准硬件上于毫秒级时间内生成多样化的退化种群，实现了仅从尖峰记录即可进行可扩展且高效的推断。总之，这项工作将DICs定位为实验观测活动与机制模型之间实用且可解释的桥梁。通过实现直接从尖峰时间快速、可扩展地重建退化种群，我们的方法为研究神经元如何利用电导变异性实现可靠计算提供了一种强有力的途径。