Observations of finely-timed spike relationships in population recordings are often used to support partial reconstruction of neural microcircuit diagrams. Here fine-timescale components of paired spike train interactions are isolated and subsequently attributed to synaptic parameters. Recent perturbation studies strengthen the case for such inferences , 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 %DIFDELCMD < {\it %%% in vivo dataset where presynaptic neurons were explicitly decoupled from network activity by juxtacellular stimulation. We then develop biophysical models of paired spike trains to reproduce the phenomenology of monosynaptic interactions observed in those data , including both fine-timescale spike-spike correlations and the irregular firing found%DIFDELCMD < {\it %%% in vivo .%DIFDELCMD < } %%% We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations. A particular focus is the justification and application of a nonparametric separation of timescale principle , based on conditional inference, to implement synaptic inference. We develop a correspondence between classes of stochastic integrate-and-fire neurons and nonparametric inference in the context of microcircuit inference . 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.
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 %DIFDELCMD < {\it %%% 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 %DIFDELCMD < {\it %%% %DIFDELCMD < } %%% 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.
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We use simulated data generated from these biophysical models to study the inference of small synaptic changes from spike data, despite the presence of rapidly-varying background network fluctuations.
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.
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We develop a correspondence between classes of stochastic integrate-and-fire neurons and nonparametric inference in the context of microcircuit inference
Using simulated data generated from the biophysical models, we characterize the regimes in which the estimators accurately identify the monosynaptic effect