Our work utilized a non-sequential simulation-based inference algorithm to provide an amortized neural density estimator, which approximates the posterior distribution for seven parameters of the adaptive exponential integrate-and-fire neuron model of the analog neuromorphic BrainScaleS-2 substrate. We constrained the large parameter space by training a binary classifier to predict parameter combinations yielding observations in regimes of interest, i.e. moderate spike counts. We compared two neural density estimators: one using handcrafted summary statistics and one using a summary network trained in combination with the neural density estimator. The summary network yielded a more focused posterior and generated posterior predictive traces that accurately captured the membrane potential dynamics. When using handcrafted summary statistics, posterior predictive traces match the included features but show deviations in the exact dynamics. The posteriors showed signs of bias and miscalibration but were still able to yield posterior predictive samples that were close to the target observations on which the posteriors were constrained. Our results validate amortized simulation-based inference as a tool for parameterizing analog neuron circuits.

翻译：本研究采用一种非序列的基于仿真的推断算法，构建了一个摊销神经密度估计器，用于近似模拟神经形态BrainScaleS-2基底上自适应指数积分发放神经元模型七个参数的后验分布。我们通过训练二元分类器预测能产生目标观测状态（即中等脉冲计数）的参数组合，从而约束了庞大的参数空间。我们比较了两种神经密度估计器：一种使用人工设计的摘要统计量，另一种使用与神经密度估计器联合训练的摘要网络。摘要网络产生了更集中的后验分布，其生成的后验预测轨迹能精确捕捉膜电位动态。当使用人工设计的摘要统计量时，后验预测轨迹虽能匹配预设特征，但在具体动态特性上存在偏差。后验分布虽呈现一定的偏差与校准误差，但仍能生成与约束后验的目标观测值高度接近的后验预测样本。我们的研究结果验证了摊销式基于仿真的推断作为模拟神经元电路参数化工具的有效性。