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Independent Sources of Quantal Variability at Single Glutamatergic Synapses
Open accessAbstract
Variability in the size of single postsynaptic responses is a feature of most central neurons, although the source of this variability is not completely understood. The dominant source of variability could be either intersynaptic or intrasynaptic. To quantitatively examine this question, a biophysically realistic model of an idealized central axospinous synapse was used to assess mechanisms underlying synaptic variability measurements. Three independent sources of variability were considered: stochasticity of postsynaptic receptors ("channel noise"), variations of glutamate concentration in the synaptic cleft (Deltaq), and differences in the potency of vesicles released from different locations on the active zone [release-location dependence (RLD)]. As expected, channel noise was small (8% of the total variance) and Deltaq was the dominant source of variability (58% of total variance). Surprisingly, RLD accounted for a significant amount of variability (36%). Our simulations show that potency of release sites decreased with a length constant of approximately 100 nm, and that receptors were not activated by release events >300 nm away, which is consistent with the observation that single active zones are rarely >300 nm. RLD also predicts that the manner in which receptors are added or removed from synapses can dramatically affect the nature of the synaptic response, with increasing receptor density being more efficient than merely increasing synaptic area. Saturation levels and synaptic geometry were also important in determining the size and shape of the distribution of amplitudes recorded at different synapses.
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“given that the probability of AMPAR activation declines with distance from glutamate release sites has previously been deduced3,16”
From A trans-synaptic nanocolumn aligns neurotransmitter release to receptors (2016) · cites this paper as [3]
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“Central to this approach is the relationship between channel opening probability and its distance from a release site, determined previously by stochastic modeling approaches3,16,47: (6)Po(r)=0.42e-r/88nm”
From A trans-synaptic nanocolumn aligns neurotransmitter release to receptors (2016) · cites this paper as [3]
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MethodDistance-dependent gradient in NMDAR-driven spine calcium signals along tapering dendrites
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“Contrary to previous works (6, 11, 14, 15), our analysis does not use the description of channels involving a time dependent multi-”
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Data-Driven Modeling of Synaptic Transmission and Integration
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“32,34 A Monte Carlo approach can also be used 100 ; in this case, a simulator such as MCell should be considered (http://www. mcell.cnl.salk.edu/).”
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“…synapses show a high variability with a diverse origin, such as the stochastic release and transmission of neurotransmitter vesicles, variations in the Glutamate concentration through synapses and the spatial heterogeneity of the synaptic response in the dendrite tree (Franks et al., 2003).”
BackgroundObjective quantification of nanoscale protein distributions
Szoboszlay, M., Kirizs, T., & Nusser, Z. (2017) · Scientific Reports · doi:10.1038/s41598-017-15695-w · Cited by 18
Targeted sensors for glutamatergic neurotransmission
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Quantal amplitude at the cone ribbon synapse can be adjusted by changes in cytosolic glutamate.
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BackgroundNew approaches for solving old problems in neuronal protein trafficking
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“The kinetics of the transporters involved binding, internalizing, and unbinding glutamate molecules and was modeled by a Markov scheme.(1) In the model for the ketogenic diet, the vesicle load was reduced to 2,000 molecules.”
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“Concomitantly to the development of these new microscopy techniques, the application of improved electron microscopy (freeze-fracture replica, pre-embedding) and synaptic modeling alluded to a potential sub-compartmentalization of the synapse [39,40].”
MethodSynaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study
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ResultNoise in Neurons and Other Constraints
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Synaptic Variability Introduces State-Dependent Modulation of Excitatory Spinal Cord Synapses
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“Among the major sources of variability in quantum response, the following should be emphasized: (1) inconstant number of neurotransmitter molecules per single quantum due to different size of vesicles and/or different concentration of neurotransmitter in them; (2) random process of diffusion of a relatively small numb…”
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Presynaptic α2δs specify synaptic gain, not synaptogenesis, in the mammalian brain
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NMDAR-Mediated Ca2+ Increase Shows Robust Information Transfer in Dendritic Spines
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A Monte Carlo simulation dissecting quantal release at the calyx of Held
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“…contain scaffolding proteins to anchor glutamate receptors for regular synaptic transmission, AMPARs are known to exist in extrasynaptic membrane near the PSD (Nair et al., 2013; Tao-Cheng et al., 2011) and likely be able to support synaptic transmission (Franks et al., 2003; Tardin et al., 2003).”
BackgroundChoi, K.-H., & Licht, S. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037399 · Cited by 3
“The stochastic conformational fluctuations that generate the variance in the number of open ion channels (‘‘noise,’’ which here refers only to the noise due to channel opening/closing) can have a substantial effect on the membrane potential of excitable cells [5–9].”
BackgroundAnalysis, Simulation, and Optimization of Stochastic Vesicle Dynamics in Synaptic Transmission
Zhang, C., & Peskin, C. S. (2019) · Communications on Pure and Applied Mathematics · doi:10.1002/cpa.21847 · Cited by 4
“The reliability of neurotransmitter release can be highly variable: since the 1950’s, experimental data from electrophysiological, molecular, and imaging studies have demonstrated that synaptic terminals can individually set their neurotransmitter release probability dynamically through local feedback regulation [9],…”
BackgroundFusion pore flux controls the rise-times of quantal synaptic responses
Jackson, M. B., Chiang, C.-W., & Cheng, J. (2024) · The Journal of General Physiology · doi:10.1085/jgp.202313484 · Cited by 3
“…Lisman, 2004), vesicle size (Bekkers et al., 1990; Karunanithi et al., 2002; Guo et al., 2015), cleft transmitter concentration (Liu et al., 1999; Franks et al., 2003), fusion pore kinetics (Guo et al., 2015), and release site-receptor registration (Uteshev and Pennefather, 1996; MacGillavry et…”
Aimone, J. B., & Misra, S. (2023) · IEEE Electron Devices Magazine · doi:10.1109/med.2023.3298873 · Cited by 3
“What is clear is that the brain exhibits stochasticity at the synaptic scale, with synaptic vesicle release being best described as stochastic for many decades [9].”
BackgroundInfluence of ionotropic receptor location on their dynamics at glutamatergic synapses
Allam, S. L., Bouteiller, J. C., Hu, E. Y., Gréget, R., Ambert, N., Bischoff, S. F., Baudry, M., & Berger, T. W. (2012) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2012.6346194 · Cited by 3
Characterizing the voltage fluctuations driven by a cluster of ligand-gated channels
Wang, J.-Z., & Fan, Y. (2021) · Physical review. E · doi:10.1103/physreve.103.052401 · Cited by 3
Ullah, G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/670844 · Cited by 3
“For example, variations of the glutamate concentration in the cleft and differences in the potency of vesicles released from different locations on the active zone are shown to be the two main contributors to the quantal variability at single glutamatergic synapse [25].”
BackgroundNonequilibrium Behavior in Neural Networks: Criticality and Optimal Performance
Torres, J. J., Johnson, S., Mejías, J. F., de Franciscis, S., & Marro, J. (2010) · Springer eBooks · doi:10.1007/978-90-481-9695-1_89 · Cited by 2
“…neurotransmitter vesicles, variation in the postsynaptic response along the dendritic tree, which in turn has several sources (e.g., variations of the glutamate concentration in the synaptic cleft) and differences in the power released from different locations on the active zone of the synapses [5].”
BackgroundCooke, R. M., Luco, S., & Parker, D. (2011) · Journal of Comparative Physiology A · doi:10.1007/s00359-011-0683-0 · Cited by 2
“…or amplitude of MEPSCs, respectively; Kirov et al. 1999; Murthy et al. 2001; Burrone and Murthy 2003), but these traditional criteria no longer provide unequivocal support for assumed presynaptic or postsynaptic sites (e.g. see Karunanithi et al. 2002; Franks et al. 2003; Rich and Wenner 2007).”
BackgroundGupta, R., Reneaux, M., & Karmeshu (2016) · PLoS ONE · doi:10.1371/journal.pone.0167505 · Cited by 2
Rubio, M. E. (2020) · Vitamins and hormones · doi:10.1016/bs.vh.2020.04.010 · Cited by 1
Stochastic Ion Channel Gating and Probabilistic Computation in Dendritic Neurons
O’Donnell, C., & Nolan, M. F. (2013) · Springer series in computational neuroscience · doi:10.1007/978-1-4614-8094-5_24 · Cited by 2
“These factors may lead to substantial variability in synaptic currents (Franks et al. 2003; Lisman et al. 2007; Faisal et al. 2008) and it will be important to establish their influence relative to that of stochastic ion channel gating.”
BackgroundVariability of quantal NMDA to AMPA current ratio in nucleus tractus solitarii neuronsPreprint
Strube, C., Gackière, F., Saliba, L., Tell, F., & Kessler, J. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/110569 · Cited by 2
“This view was mainly based on studies dealing with IAMPA variability (see for instance Franks et al., 2002; 2003).”
MethodFunctional Nano-segregation of Distinct Forms of Neurotransmission
Guzikowski, N. J., Wang, C. S., & Kavalali, E. T. (2026) · Advances in neurobiology · doi:10.1007/978-3-032-12594-1_8 · Cited by 1
Opening the black box of neural variability: From noise to mechanisms
Rentzeperis, I., Li, J., Bauer, R., & van Leeuwen, C. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106661 · Cited by 2
Biomechanics of Brain Injury: Looking to the Future
Meaney, D. F. (2014) · Springer eBooks · doi:10.1007/978-1-4939-1732-7_10 · Cited by 1
Parametric spectral analysis of nonstationary fluctuations of excitatory synaptic currents
Glavinović, M. I., Gooria, P., Aristizabal, F., & Taghirad, H. D. (2007) · Biological Cybernetics · doi:10.1007/s00422-007-0200-2 · Cited by 1
“Synaptic transmission in the excitatory synapse in the hippocampus was simulated using a Monte-Carlo method (Wahl et al. 1996; Franks et al. 2003; Ventriglia 2004).”
MethodInfluentialWeber, A. I., Shea‐Brown, E., & Rieke, F. M. (2021) · eNeuro · doi:10.1523/eneuro.0191-21.2021 · Cited by 2
“For example, identifying different sources of variability can guide the search into potential mechanisms that shape neural responses (Softky and Koch, 1993; Franks et al., 2003; Goris et al., 2014).”
BackgroundWang, J.-Z., Hu, P., & Ma, S. (2024) · European Biophysics Journal · doi:10.1007/s00249-024-01715-x · Cited by 1
“…we note that the anatomy of synapses in the central nervous system is different from that of the NMJ, that is, there are many small synapses distributed in the whole dendrite while the number of receptors in a single synapse is relatively small Franks et al. (2003); Nimchinsky et al. (2004).”
Synaptic Noise-like Activity in Hippocampal Interneurons
Stanley, D. A. (2010) · TSpace (University of Toronto) · Cited by 1
Wegner, W., Steffens, H., Gregor, C., Wolf, F., & Willig, K. I. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.23.352195 · Cited by 2
“Moreover, computational simulations predict that changes in the shape of the PSD are a way to align postsynaptic receptors to presynaptic release sites (Franks et al., 2003; Savtchenko and Rusakov, 2014).”
BackgroundFisch, K. (2011) · Elektronische Hochschulschriften der LMU München (Ludwig-Maximilians-Universität München) · doi:10.5282/edoc.13511 · Cited by 1
Srinivasan, S., Daste, S., Modi, M. N., Turner, G. C., Fleischmann, A., & Navlakha, S. (2023) · bioRxiv · doi:10.1101/2023.06.27.546757 · Cited by 1
“A reasonable level of noise for PNs would be in the range 0–50% (Bhandawat et al., 2007), and for synaptic transmission, would be in the range of 15-25% (Bekkers et al., 1990; Franks et al., 2003).”
BackgroundInfluentialRaghavachari, S., & Lisman, J. E. (2009) · Elsevier eBooks · doi:10.1016/b978-008045046-9.01427-3 · Cited by 1
Singh, P. K. (2011) · ScholarlyCommons (University of Pennsylvania)
Scaling of unitary synaptic strength in the context of network dynamics
Liu, G., & Wilson, N. R. (2005) · DSpace@MIT (Massachusetts Institute of Technology)
Plasticity of the developing glutamate synapse in the hippocampus
Abrahamsson, T. (2007) · Gothenburg University Publications Electronic Archive (Gothenburg University)
Presynaptic molecular mechanisms that control quantal neurotransmitter discharge
Castro, R. G. (2009) · Publications of the UdS (Saarland University) · doi:10.22028/d291-21248
Torres, N. J., & Varona, P. (2012)
Glutamate diffusion and AMPA receptor activation in the cerebellar glomerulus
Nielsen, T. A. (2005) · OpenGrey (Institut de l'Information Scientifique et Technique)
Metadata
- Authors
- Franks, K. M., Stevens, C. F., & Sejnowski, T. J.
- Affiliations
- Howard Hughes Medical Institute (US)
- Salk Institute for Biological Studies (US)
- ORCID
- Stevens, Charles F.
- Venue
- The Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2003-04-15
- Volume
- 23
- Issue
- 8
- Pages
- 3186-3195
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC2944019
- OpenAlex
- W2148126095
- Cited by
- 224
- References
- 55
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 17 · 2023: 7 · 2022: 9 · 2021: 14 · 2020: 6 · 2019: 11 · 2018: 10 · 2017: 8 · 2016: 7 · 2015: 16 · 2014: 10 · 2013: 11 · 2012: 14
- FWCI
- 3.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation
- Howard Hughes Medical Institute
- Topics
- Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular transport and secretion
- Keywords
- synaptic variability, glutamatergic synapses, channel noise, active zone, synaptic vesicle release, receptor saturation
- MeSH terms
- Biophysics, Computer Simulation, Models, Neurological, Monte Carlo Method, Synaptic Transmission, Particle Size, Stochastic Processes, Synapses, Synaptic Vesicles, Reproducibility of Results, Receptors, N-Methyl-D-Aspartate, Receptors, AMPA, Glutamic Acid, Excitatory Postsynaptic Potentials
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