Unveiling the Synaptic Function and Structure Using Paired Recordings From Synaptically Coupled Neurons

Qi, Guanxiao; Yang, Danqing; Ding, Chao; Feldmeyer, Dirk

doi:10.3389/fnsyn.2020.00005

Cited by 13 publications

(8 citation statements)

References 178 publications

(230 reference statements)

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“…This can be achieved by recording from multiple neurons within the same brain slice ( Table 1 ). Stimulating one neuron while recording postsynaptic responses in neighbouring neurons allows detection of excitatory and inhibitory synaptic connections ( Qi et al, 2020 ). This “multipatch” approach has been successful in characterizing properties of synapses between different local cell types in various brain regions ( Jiang et al, 2015 ; Peng et al, 2017 ; Peng et al, 2021 ).…”

Section: Neural Recording Techniquesmentioning

confidence: 99%

Current approaches to characterize micro- and macroscale circuit mechanisms of Parkinson’s disease in rodent models

Peng

Schöneberg

Esposito

et al. 2022

Experimental Neurology

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Section: Neural Recording Techniquesmentioning

confidence: 99%

Current approaches to characterize micro- and macroscale circuit mechanisms of Parkinson’s disease in rodent models

Peng

Schöneberg

Esposito

et al. 2022

Experimental Neurology

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“…Threedimensional volume electron microscopy reveals the physical connectivity among cells within volumes of cortex, but identifying different cell types within these blocks based on criteria such as long-range axonal targets or transcriptomic classes remains challenging, and the functional properties of the synapses cannot be fully ascertained (Briggman and Bock, 2012;Helmstaedter, 2013;Kubota et al, 2018). Performing paired whole-cell recordings of unitary synaptic connections followed by cell filling and morphological reconstruction is also laborious (Miles and Poncer, 1996;Thomson and Lamy, 2007;Qi et al, 2020). Importantly, these technical limitations have also hindered the ability to test for effects of perturbations of processes hypothesized to underlie the development of intracortical synaptic connections.…”

Section: Beyond Peters' Rule: the Challenge Of Identifying Additional Cell-type-dependent Developmental Mechanismsmentioning

confidence: 99%

Mechanisms Underlying Target Selectivity for Cell Types and Subcellular Domains in Developing Neocortical Circuits

Gutman-Wei

Brown

2021

Front. Neural Circuits

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The cerebral cortex contains numerous neuronal cell types, distinguished by their molecular identity as well as their electrophysiological and morphological properties. Cortical function is reliant on stereotyped patterns of synaptic connectivity and synaptic function among these neuron types, but how these patterns are established during development remains poorly understood. Selective targeting not only of different cell types but also of distinct postsynaptic neuronal domains occurs in many brain circuits and is directed by multiple mechanisms. These mechanisms include the regulation of axonal and dendritic guidance and fine-scale morphogenesis of pre- and postsynaptic processes, lineage relationships, activity dependent mechanisms and intercellular molecular determinants such as transmembrane and secreted molecules, many of which have also been implicated in neurodevelopmental disorders. However, many studies of synaptic targeting have focused on circuits in which neuronal processes target different lamina, such that cell-type-biased connectivity may be confounded with mechanisms of laminar specificity. In the cerebral cortex, each cortical layer contains cell bodies and processes from intermingled neuronal cell types, an arrangement that presents a challenge for the development of target-selective synapse formation. Here, we address progress and future directions in the study of cell-type-biased synaptic targeting in the cerebral cortex. We highlight challenges to identifying developmental mechanisms generating stereotyped patterns of intracortical connectivity, recent developments in uncovering the determinants of synaptic target selection during cortical synapse formation, and current gaps in the understanding of cortical synapse specificity.

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“…In addition to dense, EM-based reconstruction of neuronal circuits, which allows for the perhaps most comprehensive characterization of synaptic connectivity, important alternative circuit-mapping tools exist. The two most widely used techniques are viral retrograde and anterograde trans-synaptic labeling of neurons [ 6 , 26 ], and whole-cell patch-clamp recordings [ 27 ]. Patch-clamp recordings are the gold standard to infer synaptic function and have been widely applied to characterize synaptic connections among pre- and postsynaptic neurons, including the strength of their excitatory and inhibitory postsynaptic potentials (EPSPs/IPSPs), the time course of the postsynaptic responses, and the reliability of synaptic transmission.…”

Section: Introductionmentioning

confidence: 99%

“…Overall, the data obtained from patch-clamp recordings have provided essential information on the mechanisms underlying neuronal circuit computation, that, so far, could not be provided by EM-based reconstructions. This holds particularly for patch-clamp studies that were performed in vivo [ 28 ], which do not suffer from potential slicing artifacts [ 27 ]. Despite attempts to automate and scale up patch clamping procedures [ 29 ], the throughput for connectivity studies has remained comparably low.…”

Section: Introductionmentioning

confidence: 99%

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

Donner,

Bartram,

Hornauer

et al. 2024

PLoS Comput Biol

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Probing the architecture of neuronal circuits and the principles that underlie their functional organization remains an important challenge of modern neurosciences. This holds true, in particular, for the inference of neuronal connectivity from large-scale extracellular recordings. Despite the popularity of this approach and a number of elaborate methods to reconstruct networks, the degree to which synaptic connections can be reconstructed from spike-train recordings alone remains controversial. Here, we provide a framework to probe and compare connectivity inference algorithms, using a combination of synthetic ground-truth and in vitro data sets, where the connectivity labels were obtained from simultaneous high-density microelectrode array (HD-MEA) and patch-clamp recordings. We find that reconstruction performance critically depends on the regularity of the recorded spontaneous activity, i.e., their dynamical regime, the type of connectivity, and the amount of available spike-train data. We therefore introduce an ensemble artificial neural network (eANN) to improve connectivity inference. We train the eANN on the validated outputs of six established inference algorithms and show how it improves network reconstruction accuracy and robustness. Overall, the eANN demonstrated strong performance across different dynamical regimes, worked well on smaller datasets, and improved the detection of synaptic connectivity, especially inhibitory connections. Results indicated that the eANN also improved the topological characterization of neuronal networks. The presented methodology contributes to advancing the performance of inference algorithms and facilitates our understanding of how neuronal activity relates to synaptic connectivity.

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Unveiling the Synaptic Function and Structure Using Paired Recordings From Synaptically Coupled Neurons

Cited by 13 publications

References 178 publications

Current approaches to characterize micro- and macroscale circuit mechanisms of Parkinson’s disease in rodent models

Current approaches to characterize micro- and macroscale circuit mechanisms of Parkinson’s disease in rodent models

Mechanisms Underlying Target Selectivity for Cell Types and Subcellular Domains in Developing Neocortical Circuits

Ensemble learning and ground-truth validation of synaptic connectivity inferred from spike trains

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