Inferring synaptic transmission from the stochastic dynamics of the quantal content: An analytical approach

Zahra Vahdat, Oliver Gambrell, Jonas Fisch, Eckhard Friauf and Abhyudai Singh

PLOS Computational Biology, 2025, vol. 21, issue 5, 1-22

Abstract: Quantal parameters of synapses are fundamental for the temporal dynamics of neurotransmitter release, which is the basis of interneuronal communication. We formulate a general class of models that capture the stochastic dynamics of quantal content (QC), defined as the number of SV fusion events triggered by a single action potential (AP). Considering the probabilistic and time-varying nature of SV docking, undocking, and AP-triggered fusion, we derive an exact statistical distribution for the QC over time. Analyzing this distribution at steady-state and its associated autocorrelation function, we show that QC fluctuation statistics can be leveraged for inferring key presynaptic parameters, such as the probability of SV fusion (release probability) and SV replenishment at empty docking sites (refilling probability). Our model predictions are tested with electrophysiological data obtained from 50-Hz stimulation of auditory MNTB-LSO synapses in brainstem slices from juvenile mice. Our results show that while synaptic depression can be explained by low and constant refilling/release probabilities, this scenario is inconsistent with the statistics of the electrophysiological data, which show a low QC Fano factor and almost uncorrelated successive QCs. Our systematic analysis yields a model that couples a high release probability to a time-varying refilling probability to explain both the synaptic depression and its associated statistical fluctuations. In summary, we provide a general approach that exploits stochastic signatures in QCs to infer neurotransmission regulating processes that cannot be distinguished from simple analysis of averaged synaptic responses.Author summary: A quantitative understanding of interneuronal communication is imperative for elucidating the information processing mechanisms in the brain. The inherent stochastic nature of neurotransmitter release at chemical synapses has been a longstanding subject of research and can be leveraged to infer quantal parameters that govern synaptic transmission in response to a train of action potentials (APs). Building on this foundation, we have developed a general stochastic model of transmitter release with time-varying parameters that define the docking of synaptic vesicles (SVs) to a finite number of docking sites in the axon terminal, and their subsequent fusion and transmitter release. The primary contribution of our study lies in providing an exact analytical derivation of the statistical distribution of the quantal content (QC), the number of SVs fusing for each AP in a train. The proposed stochastic model is employed to investigate synaptic transmission at auditory MNTB-LSO synapses in brainstem slices from juvenile mice. Our findings demonstrate that, in contrast to a simple deterministic analysis, QC fluctuations reveal a high SV release probability per AP and a rate of SV replenishment to docking sites that is high at the onset of the AP train, but decreases for subsequent APs, thereby driving short-term plasticity.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1013067

DOI: 10.1371/journal.pcbi.1013067

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