Abstract
Depletion of synaptic neurotransmitter vesicles induces a form of short term depression in synapses throughout the nervous system. This plasticity affects how synapses filter presynaptic spike trains. The filtering properties of short term depression are often studied using a deterministic synapse model that predicts the mean synaptic response to a presynaptic spike train, but ignores variability introduced by the probabilistic nature of vesicle release and stochasticity in synaptic recovery time. We show that this additional variability has important consequences for the synaptic filtering of presynaptic information. In particular, a synapse model with stochastic vesicle dynamics suppresses information encoded at lower frequencies more than information encoded at higher frequencies, while a model that ignores this stochasticity transfers information encoded at any frequency equally well. This distinction between the two models persists even when large numbers of synaptic contacts are considered. Our study provides strong evidence that the stochastic nature neurotransmitter vesicle dynamics must be considered when analyzing the information flow across a synapse.
Highlights
Synapses act as information gates in neuronal networks
We study the synaptic filter induced by short term depression with both a stochastic model and a deterministic model of synaptic vesicle dynamics
The replacement of a neurotransmitter vesicle takes time and the depletion of vesicles can prevent subsequent action potentials from eliciting a postsynaptic response, an effect that represents a form of short term synaptic depression
Summary
Synapses act as information gates in neuronal networks. Presynaptic action potentials are communicated to postsynaptic neurons by causing synaptic neurotransmitter vesicles to release their contents, which bind to receptors on a postsynaptic neuron’s membrane, evoking a transient change in membrane conductance. After a vesicle is released, it typically takes several hundred milliseconds for it to be replaced at a synaptic contact (see Fig. 1 for a schematic of synaptic release and recovery). This refractoriness induces a form of short term synaptic depression that alters the filtering properties of synapses [1]. An accurate description of synaptic vesicle dynamics and their impact of on information transfer is necessary for a thorough understanding of coding in neuronal networks. A widely used model of synaptic depression treats vesicle release and recovery as deterministic processes [2,3,4,5,6]. The aim of our paper is to determine the impact (if any) of stochastic vesicle dynamics on the filtering properties of depressing synapses
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