Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

Barros-Zulaica, Natalí; Rahmon, John; Chindemi, Giuseppe; Perin, Rodrigo; Markram, Henry; Ramaswamy, Srikanth; Müller, Eilif

doi:10.1101/646497

Cited by 6 publications

(9 citation statements)

References 63 publications

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“…In this review, we have not covered some other properties that are of interest. One that has received attention recently is the inference of the size of the presynaptic readily-releasable vesicle pool (Abrahamsson et al, 2017; Barros-Zulaica et al, 2019). Additionally, we have focused on the binomial release model, but many synapses require different release probabilities and quantal amplitudes across release sites, which is better captured by multinomial statistics (Walmsley et al, 1988; Lanore and Silver, 2016).…”

Section: Discussionmentioning

confidence: 99%

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: 99%

Model-Based Inference of Synaptic Transmission

Bykowska

Gontier

Sax

et al. 2019

Front. Synaptic Neurosci.

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“…Therefore, the N RRP was calibrated to match the CVs of the first PSCs extracted from the raw traces of Kohus et al (2016). For a better comparison, artificial membrane noise was added to the simulated traces (see Barros‐Zulaica et al (2019) and Supplementary Methods).…”

Section: Methodsmentioning

confidence: 99%

Data‐driven integration of hippocampal CA1 synaptic physiology in silico

et al. 2020

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The anatomy and physiology of monosynaptic connections in rodent hippocampal CA1 have been extensively studied in recent decades. Yet, the resulting knowledge remains disparate and difficult to reconcile. Here, we present a data-driven approach to integrate the current state-of-the-art knowledge on the synaptic anatomy and physiology of rodent hippocampal CA1, including axo-dendritic innervation patterns, number of synapses per connection, quantal conductances, neurotransmitter release probability, and short-term plasticity into a single coherent resource. First, we undertook an extensive literature review of paired recordings of hippocampal neurons and compiled experimental data on their synaptic anatomy and physiology. The data collected in this manner is sparse and inhomogeneous due to the diversity of experimental techniques used by different groups, which necessitates the need for an integrative framework to unify these data. To this end, we extended a previously developed workflow for the neocortex to constrain a unifying in silico reconstruction of the synaptic physiology of CA1 connections. Our work identifies gaps in the existing knowledge and provides a complementary resource toward a more complete quantification of synaptic anatomy and physiology in the rodent hippocampal CA1 region.

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“…The absolute strength of synaptic transmission can be measured physiologically as the amplitude of the excitatory postsynaptic potential (EPSP), which is the product of all three quantal parameters in binomial statistics, but it remains experimentally untested for any synapse in the brain if and how the EPSP amplitude relates to PSD area. A major limitation towards assessing this important size-strength relationship in neocortex from the previous structure-function observations is that it still remains controversial how many release sites exist at single synapses [21][22][23][24][25][26] .…”

Section: Introductionmentioning

confidence: 99%

Structure and function of a neocortical synapse

Holler-Rickauer

Köstinger

Martin

et al. 2019

Preprint

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Thirty-four years since the small nervous system of the nematode C. elegans was manually reconstructed in the electron microscope (EM) 1 , 'high-throughput' EM techniques now enable the dense reconstruction of neural circuits within increasingly large brain volumes at synaptic resolution 2-6 . As with C. elegans, however, a key limitation for inferring brain function from neuronal wiring diagrams is that it remains unknown how the structure of a synapse seen in EM relates to its physiological transmission strength. Here, we related structure and function of the same synapses to bridge this gap: we combined paired whole-cell recordings of synaptically connected pyramidal neurons in slices of mouse somatosensory cortex with correlated light microscopy and high-resolution EM of all putative synaptic contacts between the neurons. We discovered a linear relationship between synapse size (postsynaptic density area) and synapse strength (excitatory postsynaptic potential amplitude), which provides an experimental foundation for assigning the actual physiological weights to synaptic connections seen in the EM. Furthermore, quantal analysis revealed that the number of vesicle release sites exceeded the number of anatomical synapses formed by a connection by a factor of at least 2.6, which challenges the current understanding of synaptic release in neocortex and suggests that neocortical synapses operate with multivesicular release, like hippocampal synapses 7-11 . Thus, neocortical synapses are more complex computational devices and may modulate their strength more flexibly than previously thought, with the corollary that the canonical neocortical microcircuitry possesses significantly higher computational power than estimated by current models.

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Estimating the Readily-Releasable Vesicle Pool Size at Synaptic Connections in a Neocortical Microcircuit

Cited by 6 publications

References 63 publications

Model-Based Inference of Synaptic Transmission

Model-Based Inference of Synaptic Transmission

Data‐driven integration of hippocampal CA1 synaptic physiology in silico

Structure and function of a neocortical synapse

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