Observations of finely-timed spike relationships in population recordings have been used to support partial reconstruction of neural microcircuit diagrams. In this approach, fine-timescale components of paired spike train interactions are isolated and subsequently attributed to synaptic parameters. Recent perturbation studies strengthen the case for such an inference, yet the complete set of measurements needed to calibrate statistical models is unavailable. To address this gap, we study features of pairwise spiking in a large-scale in vivo dataset where presynaptic neurons were explicitly decoupled from network activity by juxtacellular stimulation. We then construct biophysical models of paired spike trains to reproduce the observed phenomenology of in vivo monosynaptic interactions, including both fine-timescale spike-spike correlations and firing irregularity. A key characteristic of these models is that the paired neurons are coupled by rapidly-fluctuating background inputs. We quantify a monosynapse’s causal effect by comparing the postsynaptic train with its counterfactual, when the monosynapse is removed. Subsequently, we develop statistical techniques for estimating this causal effect from the pre- and post-synaptic spike trains. A particular focus is the justification and application of a nonparametric separation of timescale principle to implement synaptic inference. Using simulated data generated from the biophysical models, we characterize the regimes in which the estimators accurately identify the monosynaptic effect. A secondary goal is to initiate a critical exploration of neurostatistical assumptions in terms of biophysical mechanisms, particularly with regards to the challenging but arguably fundamental issue of fast, unobservable nonstationarities in background dynamics.

对人口记录中精确定时的尖峰关系的观察已被用于支持神经微电路图的部分重建。在这种方法中，成对尖峰火车相互作用的精细时间尺度组件被隔离，随后归因于突触参数。最近的扰动研究加强了这种推断的理由，但无法获得校准统计模型所需的完整测量集。为了解决这一差距，我们研究了大规模体内数据集中成对尖峰的特征，其中突触前神经元通过细胞旁刺激明确地与网络活动分离。然后，我们构建了成对尖峰序列的生物物理模型，以重现观察到的体内现象学单突触相互作用，包括精细的时间尺度尖峰相关性和发射不规则性。这些模型的一个关键特征是配对的神经元通过快速波动的背景输入耦合。当单突触被移除时，我们通过将突触后序列与其反事实进行比较来量化单突触的因果效应。随后，我们开发了统计技术来估计突触前和突触后尖峰序列的这种因果效应。一个特别的重点是非参数分离时间尺度原则的合理性和应用，以实现突触推理。使用从生物物理模型生成的模拟数据，我们描述了估计器准确识别单突触效应的机制。