Parsing out the variability of transmission at central synapses using optical quantal analysis

Soares, Cary; Trotter, Daniel; Longtin, André; Béı̈que, Jean-Claude; Naud, Richard

doi:10.1101/624692

Cited by 2 publications

(1 citation statement)

References 55 publications

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“…Of particular interest are recent advances in ultrafast optical glutamate sensors, which are enabling measurements of synaptic release with high accuracy (Helassa et al, 2018). These developments, when coupled with the statistical inference frameworks reviewed here (Costa et al, 2013; Bird et al, 2016; Ghanbari et al, 2017, but see also Soares et al, 2019), raise the possibility of accurate optical estimation of synaptic transmission properties in awake behaving animals.…”

Section: Discussionmentioning

confidence: 80%

Model-Based Inference of Synaptic Transmission

Bykowska

Gontier

Sax

et al. 2019

Front. Synaptic Neurosci.

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Synaptic computation is believed to underlie many forms of animal behavior. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.

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Section: Discussionmentioning

confidence: 80%

Model-Based Inference of Synaptic Transmission

Bykowska

Gontier

Sax

et al. 2019

Front. Synaptic Neurosci.

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Linear-Nonlinear Cascades Capture Synaptic Dynamics

Rossbroich

Trotter

Beninger

et al. 2020

Preprint

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Synaptic dynamics differ markedly across connections and strongly regulate how action potentials are being communicated. To model the range of synaptic dynamics observed in experiments, we develop a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks. Author summaryUnderstanding how information is transmitted relies heavily on knowledge of the underlying regulatory synaptic dynamics. Existing computational models for capturing such dynamics are often either very complex or too restrictive. As a result, effectively capturing the different types of dynamics observed experimentally remains a challenging problem. Here, we propose a mathematically flexible linear-nonlinear model that is capable of efficiently characterizing synaptic dynamics. We demonstrate the ability of this model to capture different features of experimentally observed data.The nervous system has evolved a communication system largely based on temporal 2 sequences of action potentials. A central feature of this communication is that action 3 potentials are communicated with variable efficacy on short (10 ms -10 s) time 4 scales [1-6]. The dynamics of synaptic efficacy at short time scales, or short-term 5 plasticity (STP), can be a powerful determinant of the flow of information, allowing the 6 same axon to communicate independent messages to different post-synaptic 7 targets [7, 8]. Properties of STP vary markedly across projections [9-11], leading to the 8idea that connections can be conceived as belonging to distinct classes [12,13] and that 9 these distinct classes shape information transmission in vivo [14][15][16]. Thus, to 10 May 29, 2020 1/25 understand the flow of information in neuronal networks, the connectome must be 11 indexed with an accurate description of STP properties. 12One approach to characterizing synaptic dynamics is to perform targeted 13 experiments and extract a summary feature, most commonly the paired-pulse 14 ratio [5,[17][18][19], whereby a synapse can be classified as short-term depressing (STD) or 15 short-term facilitating (STF). However, a single summary feature is insufficient to 16 capture the full extent of STP diversity. Longer or more complex stimulation patterns 17 are required to describe delayed facilitation onset [6], biphasic STP [20, 21] or the 18 distinction between supra-and sub-linear facilitation [22]. Such atypical STP dynamics 19 challenge the traditional dichotomy of STF and STD and suggest that more complex 20 phenotypes can exist and contribute to network function in...

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Parsing out the variability of transmission at central synapses using optical quantal analysis

Cited by 2 publications

References 55 publications

Model-Based Inference of Synaptic Transmission

Model-Based Inference of Synaptic Transmission

Linear-Nonlinear Cascades Capture Synaptic Dynamics

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