Mohácsi Máté, Török Márk Patrik, Sáray Sára, Tar Luca, Farkas Gábor, Káli Szabolcs
HUN-REN Institute of Experimental Medicine, Budapest, Hungary.
Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Budapest, Hungary.
PLoS Comput Biol. 2024 Dec 23;20(12):e1012039. doi: 10.1371/journal.pcbi.1012039. eCollection 2024 Dec.
Finding optimal parameters for detailed neuronal models is a ubiquitous challenge in neuroscientific research. In recent years, manual model tuning has been gradually replaced by automated parameter search using a variety of different tools and methods. However, using most of these software tools and choosing the most appropriate algorithm for a given optimization task require substantial technical expertise, which prevents the majority of researchers from using these methods effectively. To address these issues, we developed a generic platform (called Neuroptimus) that allows users to set up neural parameter optimization tasks via a graphical interface, and to solve these tasks using a wide selection of state-of-the-art parameter search methods implemented by five different Python packages. Neuroptimus also offers several features to support more advanced usage, including the ability to run most algorithms in parallel, which allows it to take advantage of high-performance computing architectures. We used the common interface provided by Neuroptimus to conduct a detailed comparison of more than twenty different algorithms (and implementations) on six distinct benchmarks that represent typical scenarios in neuronal parameter search. We quantified the performance of the algorithms in terms of the best solutions found and in terms of convergence speed. We identified several algorithms, including covariance matrix adaptation evolution strategy and particle swarm optimization, that consistently, without any fine-tuning, found good solutions in all of our use cases. By contrast, some other algorithms including all local search methods provided good solutions only for the simplest use cases, and failed completely on more complex problems. We also demonstrate the versatility of Neuroptimus by applying it to an additional use case that involves tuning the parameters of a subcellular model of biochemical pathways. Finally, we created an online database that allows uploading, querying and analyzing the results of optimization runs performed by Neuroptimus, which enables all researchers to update and extend the current benchmarking study. The tools and analysis we provide should aid members of the neuroscience community to apply parameter search methods more effectively in their research.
为详细的神经元模型寻找最优参数是神经科学研究中普遍存在的一项挑战。近年来，手动模型调优已逐渐被使用各种不同工具和方法的自动参数搜索所取代。然而，使用这些软件工具中的大多数并为给定的优化任务选择最合适的算法需要大量的技术专长，这使得大多数研究人员无法有效地使用这些方法。为了解决这些问题，我们开发了一个通用平台（称为Neuroptimus），它允许用户通过图形界面设置神经参数优化任务，并使用由五个不同的Python包实现的各种先进参数搜索方法来解决这些任务。Neuroptimus还提供了一些功能来支持更高级的使用，包括并行运行大多数算法的能力，这使其能够利用高性能计算架构。我们使用Neuroptimus提供的通用接口，在六个代表神经元参数搜索典型场景的不同基准上，对二十多种不同算法（及实现）进行了详细比较。我们从找到的最佳解决方案以及收敛速度方面对算法的性能进行了量化。我们确定了几种算法，包括协方差矩阵自适应进化策略和粒子群优化算法，这些算法在我们所有的用例中都能始终如一地、无需任何微调就能找到好的解决方案。相比之下，包括所有局部搜索方法在内的其他一些算法仅在最简单的用例中能提供好的解决方案，而在更复杂的问题上则完全失败。我们还通过将Neuroptimus应用于另一个涉及调整生化途径亚细胞模型参数的用例，展示了它的通用性。最后，我们创建了一个在线数据库，允许上传、查询和分析由Neuroptimus执行的优化运行结果，这使所有研究人员都能够更新和扩展当前的基准测试研究。我们提供的工具和分析应有助于神经科学界的成员在其研究中更有效地应用参数搜索方法。
