Modeling the Short-Term Dynamics ofin VivoExcitatory Spike Transmission

Abstract

Information transmission in neural networks is influenced by both short-term synaptic plasticity (STP) as well as nonsynaptic factors, such as after-hyperpolarization currents and changes in excitability. Although these effects have been widely characterized in vitro using intracellular recordings, how they interact in vivo is unclear. Here, we develop a statistical model of the short-term dynamics of spike transmission that aims to disentangle the contributions of synaptic and nonsynaptic effects based only on observed presynaptic and postsynaptic spiking. The model includes a dynamic functional connection with short-term plasticity as well as effects due to the recent history of postsynaptic spiking and slow changes in postsynaptic excitability. Using paired spike recordings, we find that the model accurately describes the short-term dynamics of in vivo spike transmission at a diverse set of identified and putative excitatory synapses, including a pair of connected neurons within thalamus in mouse, a thalamocortical connection in a female rabbit, and an auditory brainstem synapse in a female gerbil. We illustrate the utility of this modeling approach by showing how the spike transmission patterns captured by the model may be sufficient to account for stimulus-dependent differences in spike transmission in the auditory brainstem (endbulb of Held). Finally, we apply this model to large-scale multielectrode recordings to illustrate how such an approach has the potential to reveal cell type-specific differences in spike transmission in vivo. Although STP parameters estimated from ongoing presynaptic and postsynaptic spiking are highly uncertain, our results are partially consistent with previous intracellular observations in these synapses.

Linear-nonlinear cascades capture synaptic dynamics

“…The dynamics of synaptic efficacy at short time scales, or short-term plasticity (STP), can be a powerful determinant of the flow of information, allowing the same axon to communicate independent messages to different post-synaptic targets [7,8]. Properties of STP vary markedly across projections [9][10][11], leading to the idea that connections belong to distinct classes [12,13] and that these distinct classes shape information transmission in vivo [14][15][16]. Thus, to understand the flow of information in neuronal networks, structural connectivity must be indexed with an accurate description of STP properties.…”

Inferring Deep Brain Stimulation Induced Short-term Synaptic Plasticity Using Novel Dual Optimization Algorithm

“…To address the local minima problem, probabilistic approaches are proposed by Costa et al (13) to provide an estimation of the posterior distribution of the TM parameters and the uncertainty of the estimation (13,14). More recently, a new method was developed by Ghanbari et al (16,17) to estimate the TM parameters from extracellular recordings by utilizing a generalized linear model (GLM) concurrent to the TM model and reproducing the firing rate of the postsynaptic neuron. Ghanbari et al (16,17) also used a different GLM to directly estimate the STP dynamics, a model that can be considered as an alternative to the TM model.…”

“…More recently, a new method was developed by Ghanbari et al (16,17) to estimate the TM parameters from extracellular recordings by utilizing a generalized linear model (GLM) concurrent to the TM model and reproducing the firing rate of the postsynaptic neuron. Ghanbari et al (16,17) also used a different GLM to directly estimate the STP dynamics, a model that can be considered as an alternative to the TM model. Rossbroich et al (18) introduced a new synaptic model which represents short-term dynamics by combining an exponential kernel with a non-linear readout function.…”

Identifiability of a Binomial Synapse

“…It is also possible to perform model-based inference of synaptic parameters based on post-synaptic spike trains instead of EPSCs, as inGhanbari et al (2017Ghanbari et al ( , 2020 Frontiers in Computational Neuroscience | www.frontiersin.org…”

“… 1 It is also possible to perform model-based inference of synaptic parameters based on post-synaptic spike trains instead of EPSCs, as in Ghanbari et al ( 2017 , 2020 ) …”