Tracking fast and slow changes in synaptic weights from simultaneously observed pre- and postsynaptic spiking

TL;DR

This paper introduces an extended Generalized Linear Model to accurately infer and track both fast and slow synaptic weight changes from observed pre- and postsynaptic spiking activity, capturing multiple timescales of synaptic plasticity.

Contribution

The authors develop a novel model that simultaneously estimates short- and long-term synaptic changes from spike data, improving inference accuracy over existing methods.

Findings

Model accurately recovers time-varying synaptic weights in simulations.

Simultaneous tracking of fast and slow changes prevents spurious inferences.

Application to experimental data reveals importance of modeling multiple timescales.

Abstract

Synapses change on multiple timescales, ranging from milliseconds to minutes, due to a combination of both short- and long-term plasticity. Here we develop an extension of the common Generalized Linear Model to infer both short- and long-term changes in the coupling between a pre- and post-synaptic neuron based on observed spiking activity. We model short-term synaptic plasticity using additive effects that depend on the presynaptic spike timing, and we model long-term changes in both synaptic weight and baseline firing rate using point process adaptive smoothing. Using simulations, we first show that this model can accurately recover time-varying synaptic weights 1) for both depressing and facilitating synapses, 2) with a variety of long-term changes (including realistic changes, such as due to STDP), 3) with a range of pre- and post-synaptic firing rates, and 4) for both excitatory and inhibitory synapses. We then apply our model to two experimentally recorded putative synaptic connections. We find that simultaneously tracking fast changes in synaptic weights, slow changes in synaptic weights, and unexplained variations in baseline firing is essential. Omitting any one of these factors can lead to spurious inferences for the others. Altogether, this model provides a flexible framework for tracking short- and long-term variation in spike transmission.