As multielectrode array technology increases in popularity, accessible analytical tools become necessary. Simultaneous recordings from multiple neurons may produce huge amounts of information. Traditional tools based on classical statistics are either insufficient to analyse multiple spike trains or sophisticated and expensive in computing terms. In this communication, we put to the test the idea that AI algorithms may be useful to gather information about the effective connectivity of neurons in local nuclei at a relatively low computing cost. To this end, we decided to explore the capability of the algorithm C5.0 to retrieve information from a large series of spike trains obtained from a simulated neuronal circuit with a known structure. Combinatory, iterative and recursive processes using C5.0 were built to examine possibilities of increasing the performance of a direct application of the algorithm. Furthermore, we tested the applicability of these processes to a smaller dataset obtained from original biological recordings whose structure is unknown. These were obtained in house from a mouse in vitro preparation of the spinal cord. Results show that this algorithm can retrieve neurons monosynaptic connected to the target in simulated datasets within a single run. Iterative and recursive processes can identify monosynaptic neurons and disynaptic neurons under favourable conditions. Application of these processes to the biological dataset gives clues to identify neurons monosynaptically connected to the target. We conclude that the work presented provides substantial proof of concept for the potential use of AI algorithms to the study of effective connectivity.

中文翻译：

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在神经回路中发现有效的连通性：基于机器学习方法的分析

随着多电极阵列技术的普及，可访问的分析工具变得必要。来自多个神经元的同时记录可能会产生大量信息。基于经典统计数据的传统工具要么不足以分析多个峰值序列，要么在计算方面非常复杂且昂贵。在这次交流中，我们测试了AI算法可能以相对较低的计算成本来收集有关局部原子核中神经元有效连接性的信息的想法。为此，我们决定探索算法C5.0的功能，以从一系列已知结构的模拟神经元电路获得的大量尖峰序列中检索信息。使用C5的组合，迭代和递归过程。建立0以检验提高算法直接应用性能的可能性。此外，我们测试了这些过程对从结构未知的原始生物记录中获得的较小数据集的适用性。这些是从小鼠的脊髓体外制备物中获得的。结果表明，该算法可以在单次运行中检索到模拟数据集中与目标连接的单突触神经元。迭代和递归过程可以在有利条件下识别单突触神经元和突触神经元。将这些过程应用于生物学数据集可提供线索，以识别单突触连接到目标的神经元。