通过数据嵌入和基于模拟的推理在神经形态硬件上复现 AdEx 动力学
Reproduction of AdEx dynamics on neuromorphic hardware through data embedding and simulation-based inference
AI总结：
本文针对 BrainScaleS-2 上复杂神经元模型参数校准难题，结合 autoencoder 特征提取与 SNPE 基于模拟的推理，实现 AdEx 动力学复现并近似参数后验分布。
摘要：
开发物理系统的机制模型对于理解其行为以及提出可通过实验验证的预测至关重要。这些模型的校准（尤其是针对复杂系统）由于手动参数调整不切实际，需要自动化优化方法。在本研究中，我们使用 autoencoder（自编码器）从 BrainScaleS-2 神经形态系统上仿真的复杂神经元模型的膜轨迹中自动提取相关特征，随后利用 sequential neural posterior estimation（SNPE，序列神经后验估计）这一基于模拟的推理算法来近似神经元参数的后验分布。我们的结果表明，autoencoder 能够从观测到的膜轨迹中提取基本特征，借助这些特征，SNPE 算法能够找到后验分布的近似。这表明 autoencoder 与 SNPE 算法相结合是一种很有前景的复杂系统优化方法。
英文摘要：
The development of mechanistic models of physical systems is essential for understanding their behavior and formulating predictions that can be validated experimentally. Calibration of these models, especially for complex systems, requires automated optimization methods due to the impracticality of manual parameter tuning. In this study, we use an autoencoder to automatically extract relevant features from the membrane trace of a complex neuron model emulated on the BrainScaleS-2 neuromorphic system, and subsequently leverage sequential neural posterior estimation (SNPE), a simulation-based inference algorithm, to approximate the posterior distribution of neuron parameters. Our results demonstrate that the autoencoder is able to extract essential features from the observed membrane traces, with which the SNPE algorithm is able to find an approximation of the posterior distribution. This suggests that the combination of an autoencoder with the SNPE algorithm is a promising optimization method for complex systems.
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