Tracking Fast and Slow Changes in Synaptic Weights from Simultaneously Observed Pre- and Postsynaptic Spiking

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 postsynaptic 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 postsynaptic 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.

Predictable fluctuations in excitatory synaptic strength due to natural variation in presynaptic firing rate

“…Here we model the time-varying synaptic effect as a function of presynaptic ISI by fitting a generalized bilinear model (GBLM) to the postsynaptic spike trains. This model has been previously described (Ghanbari et al, 2017; Wei and Stevenson, 2021) and extends previous models of coupled GLMs (Harris et al, 2003; Truccolo et al, 2005; Pillow et al, 2008; Rebesco et al, 2010) to account for fluctuating excitability and plasticity. Briefly, for each connection, we model the postsynaptic firing rate γ at time t as y coup = n pre ∗ x coup where n pre and n post are the pre- and postsynaptic spike trains, respectively.…”

“…Here β 0 ( t ) represents a time-varying baseline firing rate, which is estimated using an adaptive filtering algorithm (Wei and Stevenson, 2021). β s y slow ( t ) accounts for slow fluctuations that are shared by the presynaptic neuron and postsynaptic neuron, where y slow denotes a set of covariates generated by filtering the presynaptic spike train (cubic B spline functions with 4 equally spaced knots over 150 ms x slow ).…”

“…By focusing on specific presynaptic spike patterns, such as pairs with specific inter-spike intervals (ISI), previous studies have shown how synaptic efficacy can vary depending on features of presynaptic spike timing (Usrey et al, 2000; Swadlow and Gusev, 2001; English et al, 2017). Modeling individual spike transmission probabilities for the entire spike train can also reveal how presynaptic spike features, as well as different types of plasticity, interact (Ghanbari et al, 2017, 2020; Song et al, 2018; Wei and Stevenson, 2021). Together, descriptive and model-based approaches can track synaptic dynamics in vivo (Swadlow and Gusev, 2001; English et al, 2017; Ghanbari et al, 2017, 2020) and reveal changes associated with learning, behavior, or stimuli (Fujisawa et al, 2008; Ghanbari et al, 2020; McKenzie et al, 2021).…”

Dynamic modeling of spike count data with Conway-Maxwell Poisson variability

“…However, the lack of closed-form moments for the CMP distribution makes sampling computationally intensive. Here, to estimate Q robustly and quickly, we instead assume Q is diagonal and estimate it by maximizing the prediction likelihood in the filtering stage, as in [27].…”

Predictable Fluctuations in Excitatory Synaptic Strength Due to Natural Variation in Presynaptic Firing Rate

“…By focusing on specific presynaptic spike patterns, such as pairs with specific inter-spike intervals (ISI), previous studies have shown how synaptic efficacy can vary depending on features of presynaptic spike timing (Usrey et al, 2000;Swadlow and Gusev, 2001;English et al, 2017). Modeling individual spike transmission probabilities for the entire spike train can also reveal how presynaptic spike features, as well as different types of plasticity, interact (Ghanbari et al, 2017(Ghanbari et al, , 2020Song et al, 2018;Wei and Stevenson, 2021).…”