条件流匹配用于单神经元电生理学：捕捉跨刺激的多模态响应

中文摘要 AI 辅助

大脑神经元表现出丰富的电生理动力学，相同的重复刺激会从同一细胞引发非常不同的电压响应。在生物物理详细模型中，一种常见的方法是通过确定性参数化的集成来捕捉这种变异性，但代价是数十万CPU小时的计算成本。现有的机器学习替代模型继承了同样的局限性，即一个刺激被映射到单一的电压响应。我们通过学习单神经元电生理学的条件生成模型来解决这一挑战，使用以输入电流为条件的速度场的流匹配方法。在两种人类皮层中间神经元类型的生物物理详细模型上，生成的响应紧密再现了电生理特征分布、尖峰时间结构和兴奋性概况，甚至匹配了相应人类皮层神经元的实验记录。在放电阈值附近，放电和不放电响应在同一刺激幅度下共存，而在高幅度下，集成可能在去极化阻滞附近分裂为低放电和高放电模式。我们证明我们的模型在每种情况下都能恢复这两种模式，而神经算子基线在阈值附近抑制尖峰，并在去极化阻滞时模糊了模式之间的差距。

英文摘要

Neurons of the brain exhibit a rich repertoire of electrophysiology dynamics with the same repeated stimulus eliciting very different voltage responses from the same cell. One common approach in biophysically detailed models is to capture this variability through ensembles of deterministic parametrizations, at a cost of hundreds of thousands of CPU hours. Existing machine learning surrogates inherit the same limitation, where a stimulus is mapped to a single voltage response. We address this challenge by learning a conditional generative model for single-neuron electrophysiology, using flow matching with a velocity field conditioned on the input current. On biophysically detailed models of two human cortical interneuron types, the generated responses closely reproduce the electrophysiological feature distributions, spike-time structure, and excitability profiles, even matching the experimental recordings from the corresponding human cortical neurons. Near the firing threshold, firing and non-firing responses coexist at the same stimulus amplitude, and at high amplitudes, ensembles may split into low- and high-firing modes near depolarization block. We show that our model recovers both modes in each case, while a neural operator baseline suppresses spiking near threshold and blurs the gap between modes at depolarization block.

发表机构

• University of Cambridge（剑桥大学）
• Cedars-Sinai Medical Center（西达赛奈医疗中心）
• California Institute of Technology（加州理工学院）
• Archimedes AI, Athena Research Center（阿基米德人工智能，雅典娜研究中心）

机构由 AI 辅助整理，请以论文原文为准。