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Transmission of temporally correlated spike trains through synapses with short-term depression
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Bird, Alex D. and Richardson, Magnus J. E. (2018) Transmission of temporally correlated spike trains through synapses with short-term depression. PLoS Computational Biology, 14 (6). e1006232. doi:10.1371/journal.pcbi.1006232
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Official URL: https://doi.org/10.1371/journal.pcbi.1006232
Abstract
Short-term synaptic depression, caused by depletion of releasable neurotransmitter, modulates the strength of neuronal connections in a history-dependent manner. Quantifying the statistics of synaptic transmission requires stochastic models that link probabilistic neurotransmitter release with presynaptic spike-train statistics. Common approaches are to model the presynaptic spike train as either regular or a memory-less Poisson process: few analytical results are available that describe depressing synapses when the afferent spike train has more complex, temporally correlated statistics such as bursts. Here we present a series of analytical results—from vesicle release-site occupancy statistics, via neurotransmitter release, to the post-synaptic voltage mean and variance—for depressing synapses driven by correlated presynaptic spike trains. The class of presynaptic drive considered is that fully characterised by the inter-spike-interval distribution and encompasses a broad range of models used for neuronal circuit and network analyses, such as integrate-and-fire models with a complete post-spike reset and receiving sufficiently short-time correlated drive. We further demonstrate that the derived post-synaptic voltage mean and variance allow for a simple and accurate approximation of the firing rate of the post-synaptic neuron, using the exponential integrate-and-fire model as an example. These results extend the level of biological detail included in models of synaptic transmission and will allow for the incorporation of more complex and physiologically relevant firing patterns into future studies of neuronal networks.
| Item Type: | Journal Article | ||||||||||||
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| Subjects: | Q Science > QA Mathematics Q Science > QP Physiology |
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| Divisions: | Faculty of Science > Mathematics Faculty of Science > Centre for Systems Biology |
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| Library of Congress Subject Headings (LCSH): | Neural circuitry, Neural transmission, Poisson processes | ||||||||||||
| Journal or Publication Title: | PLoS Computational Biology | ||||||||||||
| Publisher: | Public Library of Science | ||||||||||||
| ISSN: | 1553-7358 | ||||||||||||
| Official Date: | 22 June 2018 | ||||||||||||
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| Date of first compliant deposit: | 28 June 2018 | ||||||||||||
| Volume: | 14 | ||||||||||||
| Number: | 6 | ||||||||||||
| Article Number: | e1006232 | ||||||||||||
| DOI: | 10.1371/journal.pcbi.1006232 | ||||||||||||
| Status: | Peer Reviewed | ||||||||||||
| Publication Status: | Published | ||||||||||||
| Access rights to Published version: | Open Access | ||||||||||||
| RIOXX Funder/Project Grant: |
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