  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 are
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.
