Monosynaptic inference via finely-timed spikes.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Neural microcircuit diagrams are partially reconstructed using spike timing in population recordings.
• Inferring synaptic parameters from spike train interactions is crucial but lacks complete measurement data.

Purpose of the Study:

• To develop methods for inferring monosynaptic causal effects from spike trains.
• To address the gap in measurements needed for calibrating statistical models of neural interactions.

Main Methods:

• Studied pairwise spiking in a large-scale in vivo dataset with juxtacellular stimulation.
• Constructed biophysical models of paired spike trains with fluctuating background inputs.
• Developed statistical techniques, including a nonparametric separation of timescale principle, for synaptic inference.

Main Results:

• Quantified monosynapse causal effect by comparing postsynaptic trains with and without the synapse.
• Characterized estimator accuracy using simulated data from biophysical models.
• Identified regimes where estimators accurately identify monosynaptic effects.

Conclusions:

• The study provides statistical techniques for estimating causal effects of monosynapses.
• Highlights the importance of background inputs and addresses challenges of nonstationarities in neural data.
• Advances understanding of synaptic inference and neural microcircuitry.