The BrainScaleS-2 (BSS-2) system implements physical models of neurons as well as synapses and aims for an energy-efficient and fast emulation of biological neurons. When replicating neuroscientific experiment results, a major challenge is finding suitable model parameters. This study investigates the suitability of the sequential neural posterior estimation (SNPE) algorithm for parameterizing a multi-compartmental neuron model emulated on the BSS-2 analog neuromorphic hardware system. In contrast to other optimization methods such as genetic algorithms or stochastic searches, the SNPE algorithms belongs to the class of approximate Bayesian computing (ABC) methods and estimates the posterior distribution of the model parameters; access to the posterior allows classifying the confidence in parameter estimations and unveiling correlation between model parameters. In previous applications, the SNPE algorithm showed a higher computational efficiency than traditional ABC methods. For our multi-compartmental model, we show that the approximated posterior is in agreement with experimental observations and that the identified correlation between parameters is in agreement with theoretical expectations. Furthermore, we show that the algorithm can deal with high-dimensional observations and parameter spaces. These results suggest that the SNPE algorithm is a promising approach for automating the parameterization of complex models, especially when dealing with characteristic properties of analog neuromorphic substrates, such as trial-to-trial variations or limited parameter ranges.

翻译：BrainScaleS-2 (BSS-2) 系统实现了神经元与突触的物理模型，旨在以高能效方式快速模拟生物神经元。在复现神经科学实验结果时，主要挑战在于寻找合适的模型参数。本研究探讨了序贯神经后验估计（SNPE）算法在BSS-2模拟神经形态硬件系统上仿真的多室神经元模型参数化中的适用性。与遗传算法或随机搜索等其他优化方法不同，SNPE算法属于近似贝叶斯计算（ABC）方法范畴，通过估计模型参数的后验分布；获取后验分布可对参数估计的置信度进行分类，并揭示模型参数间的相关性。在以往应用中，SNPE算法展现出比传统ABC方法更高的计算效率。针对我们的多室模型，我们证明了近似后验与实验观测结果一致，且参数间识别出的相关性与理论预期相符。此外，我们展示了该算法能够处理高维观测空间与参数空间。这些结果表明，SNPE算法是实现复杂模型参数自动化的可行方法，尤其适用于处理模拟神经形态基底的特性，如试验间变异或参数范围受限等情况。