Improved signaling as a result of randomness in synaptic vesicle release

Abstract

The probabilistic nature of neurotransmitter release in synapses is believed to be one of the most significant sources of noise in the central nervous system. We show how p 0 , the probability of release per docked vesicle when an action potential arrives, affects the dynamics of the rate of vesicle release in response to changes in the rate of arrival of action potentials. Furthermore, we examine the theoretical capability of a synapse in the estimation of desired signals using information from the stochastic vesicle release events under the framework of optimal linear filter theory. We find that a small p 0 , such as 0.1, reduces the error in the reconstruction of the input, or in the reconstruction of the time derivative of the input, from the time series of vesicle release events. Our results imply that the probabilistic nature of synaptic vesicle release plays a direct functional role in synaptic transmission.synaptic transmission | stochastic vesicle release | release probability | optimal filter R andomness is present in almost all levels of all nervous systems, such as in ionic channels of individual neurons, in synapses between neurons, and in environmental stimuli. The probabilistic nature of the synaptic vesicle release process is believed to be one of the most significant sources of randomness. Stochastic vesicle release affects information transfer from a presynaptic neuron to a postsynaptic neuron, and hence may play not only an important role in synaptic plasticity (1, 2) but also a significant role in determining the functionality of certain synapses, a point we will argue in this article. The functional role of stochastic vesicle release in synaptic transmission is likely to be more significant in those synapses in the central nervous system, where the size of the readily releasable vesicle pool is usually smaller than those in the periphery.Synaptic vesicle release has only recently been studied in a probabilistic or information-theoretic manner (3-5). A systematic perspective on how stochastic vesicle release affects neural code processing at synapses is still lacking. On the experimental front, a comprehensive, quantitative knowledge about the nature of vesicle docking, priming, fusing, undocking, replenishing, and recycling is far from complete, and hence a biophysically detailed model of the entire vesicle release process is not yet possible.Despite current limited understanding of the synaptic vesicle release process, a probabilistic model that captures the essential elements of that process can still be built to study the effect of various sources of randomness on synaptic transmission. Rosenbaum et al. (3) recently constructed such a model to study how variability in vesicle dynamics affects information transfer from one neuron to another. They found that the depletion of docked vesicles at higher rates of arrival of action potentials makes the synapse act as a highpass filter from a signal processing point of view. Motivated by Rosenbaum et al.'s (3) model of the presynaptic vesicle r...

Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

“…We succeeded on five connections from which we concluded that on average the number of vesicles ready to be released is between 2 and 3. Although some studies did not support our result (Silver et al, 2003), many others support the idea of MVR as a process that happens in the neocortex (Zhang and Peskin, 2015) and actually that not necessarily the number of vesicles does not necessarily have to be the same, but that it could change from one layer to another (Brémaud et al, 2007). Other studies agreed with our result, suggesting that we achieve this result because the data that we were comparing with come from the somatosensory cortex (Huang et al, 2010).…”

Analysis, Simulation, and Optimization of Stochastic Vesicle Dynamics in Synaptic Transmission

“…That is, for sufficiently fast arrival of action potentials, the steady-state rate of vesicle release does not depend on the rate of arrival of action potentials, since it is limited by the maximum rate at which docking can occur. This shows that in the regime of fast arrival of action potentials, a synapse with a finite number of docking sites behaves in a similar manner to a model synapse with an unlimited number of docking sites [76]. On the other hand, as s !…”

“…As a starting point for this discussion, consider the case of an unlimited number of docking sites with no undocking. This model, which was the focus of our previous paper [76], has some beautiful mathematical properties. In particular, the mean rate of release of synaptic vesicles is completely insensitive to the mean rate of arrival of action potentials, so that the model synapse responds only in a transient way to changes in the rate of arrival of action potentials.…”

“…In a recent paper [76] we considered an idealized model synapse with an unlimited number of vesicle docking sites and no undocking, in which we assumed that vesicle docking occurs by a homogeneous Poisson process with mean rate˛0, that presynaptic action potentials arrive by a stochastic process with mean rate s.t / > 0, and that each vesicle that is docked has a probability p 0 to be released upon the arrival of each action potential, independently of other docked vesicles. In this idealized case, we found that the expected rate of vesicle release r.t / is governed by d dt…”

“…We assume that the presynaptic spike trains T k are generated by the following deterministic integrate-and-fire model with no leakage current over the time interval OEt 0 ; t end (see Algorithm 2 in Appendix B). Note that in our recent paper [76] we used the "faithful copy neuron" approach [27,66], which allows the user to generate a spike train with a user-prescribed interspike interval distribution such that the expected spike rate is equal to S.t / at any given time t . Here, however, we use a simple integrate-and-fire model for our numerical examples to remove unnecessary distraction from the stochastic processes of greatest interest: the stochastic docking, undocking, and release of vesicles.…”