Processing neural activity to reconstruct network connectivity is a central focus of neuroscience, yet the spatiotemporal requisites of biological nervous systems are challenging for current neuronal sensing modalities. Consequently, methods that leverage limited data to successfully infer synaptic connections, predict activity at single unit resolution, and decipher their effect on whole systems, can uncover critical information about neural processing. Despite the emergence of powerful methods for inferring connectivity, network reconstruction based on temporally subsampled data remains insufficiently unexplored. We infer synaptic weights by processing firing rates within variable time bins for a heterogeneous feed-forward network of excitatory, inhibitory, and unconnected units. We assess classification and optimize model parameters for postsynaptic spike train reconstruction. We test our method on a physiological network of leaky integrate-and-fire neurons displaying bursting patterns and assess prediction of postsynaptic activity from microelectrode array data. Results reveal parameters for improved prediction and performance and suggest that lower resolution data and limited access to neurons can be preferred. Recent computational methods demonstrate highly improved reconstruction of connectivity from networks of parallel spike trains by considering spike lag, time-varying firing rates, and other underlying dynamics. However, these methods insufficiently explore temporal subsampling representative of novel data types. We provide a framework for reverse engineering neural networks from data with limited temporal quality, describing optimal parameters for each bin size, which can be further improved using non-linear methods and applied to more complicated readouts and connectivity distributions in multiple brain circuits.

中文翻译：

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从暂时分箱的尖峰序列推断网络连接

处理神经活动以重建网络连接是神经科学的核心焦点，但生物神经系统的时空要求对当前的神经元传感方式构成了挑战。因此，利用有限的数据成功推断突触连接、预测单个单元分辨率的活动并破译其对整个系统的影响的方法可以揭示有关神经处理的关键信息。尽管出现了用于推断连通性的强大方法，但基于时间二次采样数据的网络重建仍未得到充分探索。我们通过处理兴奋性、抑制性和未连接单元的异构前馈网络的可变时间区间内的放电率来推断突触权重。我们评估分类并优化突触后尖峰序列重建的模型参数。我们在显示爆发模式的泄漏整合和激发神经元的生理网络上测试我们的方法，并评估微电极阵列数据对突触后活动的预测。结果揭示了改进预测和性能的参数，并表明可以优先选择较低分辨率的数据和对神经元的有限访问。最近的计算方法表明，通过考虑尖峰滞后、时变放电率和其他潜在动态，并行尖峰序列网络的连接性重建得到了极大改进。然而，这些方法不足以探索代表新数据类型的时间子采样。我们提供了一个框架，用于根据时间质量有限的数据对神经网络进行逆向工程，描述每个箱大小的最佳参数，可以使用非线性方法进一步改进该框架，并将其应用于多个大脑回路中更复杂的读数和连接分布。