title: Quantifying Repetitive Transmission at Chemical Synapses: A Generative-Model Approach.
abstract: The dependence of the synaptic responses on the history of activation and their large variability are both distinctive features of repetitive transmission at chemical synapses. Quantitative investigations have mostly focused on trial-averaged responses to characterize dynamic aspects of the transmission--thus disregarding variability--or on the fluctuations of the responses in steady conditions to characterize variability--thus disregarding dynamics. We present a statistically principled framework to quantify the dynamics of the probability distribution of synaptic responses under arbitrary patterns of activation. This is achieved by constructing a generative model of repetitive transmission, which includes an explicit description of the sources of stochasticity present in the process. The underlying parameters are then selected via an expectation-maximization algorithm that is exact for a large class of models of synaptic transmission, so as to maximize the likelihood of the observed responses. The method exploits the information contained in the correlation between responses to produce highly accurate estimates of both quantal and dynamic parameters from the same recordings. The method also provides important conceptual and technical advances over existing state-of-the-art techniques. In particular, the repetition of the same stimulation in identical conditions becomes unnecessary. This paves the way to the design of optimal protocols to estimate synaptic parameters, to the quantitative comparison of synaptic models over benchmark datasets, and, most importantly, to the study of repetitive transmission under physiologically relevant patterns of synaptic activation.
authors.affiliation: Unité d'Imagerie Dynamique du Neurone , Institut Pasteur, Paris, France.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Barri
authors.affiliation: Caritas St. Elizabeth's Center, Tufts University , Boston, MA, USA.
authors.forename: Yun
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Centre National de la Recherche Scientifique, UMR 8119, Paris, France; Cerebral Dynamics, Plasticity and Learning, Centre de Neurophysique, Physiologie et Pathologie, Université Descartes, Paris, France.
authors.forename: David
authors.initials: D
authors.lastname: Hansel
authors.affiliation: Centre National de la Recherche Scientifique, UMR 8119, Paris, France; Cerebral Dynamics, Plasticity and Learning, Centre de Neurophysique, Physiologie et Pathologie, Université Descartes, Paris, France.
authors.forename: Gianluigi
authors.initials: G
authors.lastname: Mongillo
mesh_terms: D000818:Animals; D000831:Animals, Newborn; D005289:Ferrets; D066298:In Vitro Techniques; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D017397:Prefrontal Cortex / Q000502:physiology*; D011336:Probability; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology*; D050781:Transcranial Magnetic Stimulation
keywords: expectation-maximization; generative modeling; quantal analysis; repetitive transmission; short-term plasticity