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      Anatomical and Functional Connectivity at the Dendrodendritic Reciprocal Mitral Cell–Granule Cell Synapse: Impact on Recurrent and Lateral Inhibition

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          Abstract

          In the vertebrate olfactory bulb, reciprocal dendrodendritic interactions between its principal neurons, the mitral and tufted cells, and inhibitory interneurons in the external plexiform layer mediate both recurrent and lateral inhibition, with the most numerous of these interneurons being granule cells. Here, we used recently established anatomical parameters and functional data on unitary synaptic transmission to simulate the strength of recurrent inhibition of mitral cells specifically from the reciprocal spines of rat olfactory bulb granule cells in a quantitative manner. Our functional data allowed us to derive a unitary synaptic conductance on the order of 0.2 nS. The simulations predicted that somatic voltage deflections by even proximal individual granule cell inputs are below the detection threshold and that attenuation with distance is roughly linear, with a passive length constant of 650 μm. However, since recurrent inhibition in the wake of a mitral cell action potential will originate from hundreds of reciprocal spines, the summated recurrent IPSP will be much larger, even though there will be substantial mutual shunting across the many inputs. Next, we updated and refined a preexisting model of connectivity within the entire rat olfactory bulb, first between pairs of mitral and granule cells, to estimate the likelihood and impact of recurrent inhibition depending on the distance between cells. Moreover, to characterize the substrate of lateral inhibition, we estimated the connectivity via granule cells between any two mitral cells or all the mitral cells that belong to a functional glomerular ensemble (i.e., which receive their input from the same glomerulus), again as a function of the distance between mitral cells and/or entire glomerular mitral cell ensembles. Our results predict the extent of the three regimes of anatomical connectivity between glomerular ensembles: high connectivity within a glomerular ensemble and across the first four rings of adjacent glomeruli, substantial connectivity to up to eleven glomeruli away, and negligible connectivity beyond. Finally, in a first attempt to estimate the functional strength of granule-cell mediated lateral inhibition, we combined this anatomical estimate with our above simulation results on attenuation with distance, resulting in slightly narrowed regimes of a functional impact compared to the anatomical connectivity.

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          Most cited references113

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          SeeDB: a simple and morphology-preserving optical clearing agent for neuronal circuit reconstruction.

          We report a water-based optical clearing agent, SeeDB, which clears fixed brain samples in a few days without quenching many types of fluorescent dyes, including fluorescent proteins and lipophilic neuronal tracers. Our method maintained a constant sample volume during the clearing procedure, an important factor for keeping cellular morphology intact, and facilitated the quantitative reconstruction of neuronal circuits. Combined with two-photon microscopy and an optimized objective lens, we were able to image the mouse brain from the dorsal to the ventral side. We used SeeDB to describe the near-complete wiring diagram of sister mitral cells associated with a common glomerulus in the mouse olfactory bulb. We found the diversity of dendrite wiring patterns among sister mitral cells, and our results provide an anatomical basis for non-redundant odor coding by these neurons. Our simple and efficient method is useful for imaging intact morphological architecture at large scales in both the adult and developing brains.
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            NEURON and Python

            The NEURON simulation program now allows Python to be used, alone or in combination with NEURON's traditional Hoc interpreter. Adding Python to NEURON has the immediate benefit of making available a very extensive suite of analysis tools written for engineering and science. It also catalyzes NEURON software development by offering users a modern programming tool that is recognized for its flexibility and power to create and maintain complex programs. At the same time, nothing is lost because all existing models written in Hoc, including graphical user interface tools, continue to work without change and are also available within the Python context. An example of the benefits of Python availability is the use of the xml module in implementing NEURON's Import3D and CellBuild tools to read MorphML and NeuroML model specifications.
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              Shunting inhibition improves robustness of gamma oscillations in hippocampal interneuron networks by homogenizing firing rates.

              Networks of GABAergic neurons are key elements in the generation of gamma oscillations in the brain. Computational studies suggested that the emergence of coherent oscillations requires hyperpolarizing inhibition. Here, we show that GABA(A) receptor-mediated inhibition in mature interneurons of the hippocampal dentate gyrus is shunting rather than hyperpolarizing. Unexpectedly, when shunting inhibition is incorporated into a structured interneuron network model with fast and strong synapses, coherent oscillations emerge. In comparison to hyperpolarizing inhibition, networks with shunting inhibition show several advantages. First, oscillations are generated with smaller tonic excitatory drive. Second, network frequencies are tuned to the gamma band. Finally, robustness against heterogeneity in the excitatory drive is markedly improved. In single interneurons, shunting inhibition shortens the interspike interval for low levels of drive but prolongs it for high levels, leading to homogenization of neuronal firing rates. Thus, shunting inhibition may confer increased robustness to gamma oscillations in the brain.
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                Author and article information

                Contributors
                Journal
                Front Neural Circuits
                Front Neural Circuits
                Front. Neural Circuits
                Frontiers in Neural Circuits
                Frontiers Media S.A.
                1662-5110
                22 July 2022
                2022
                : 16
                : 933201
                Affiliations
                [1] 1School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM) , Tehran, Iran
                [2] 2Division of Cerebral Circuitry, National Institute for Physiological Sciences (NIPS) , Okazaki, Japan
                [3] 3Neurophysiology, Institute of Zoology, Regensburg University , Regensburg, Germany
                Author notes

                Edited by: Gordon M. Shepherd, Yale University, United States

                Reviewed by: Charles A. Greer, Yale University, United States; Shawn D. Burton, Lehigh University, United States

                *Correspondence: Veronica Egger, Veronica.Egger@ 123456ur.de
                Article
                10.3389/fncir.2022.933201
                9355734
                35937203
                6ee91ea4-cd80-4e17-a44b-7441ceb1efd5
                Copyright © 2022 Aghvami, Kubota and Egger.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 30 April 2022
                : 27 May 2022
                Page count
                Figures: 5, Tables: 2, Equations: 10, References: 113, Pages: 18, Words: 15423
                Categories
                Neural Circuits
                Original Research

                Neurosciences
                olfactory bulb,recurrent inhibition,lateral inhibition,network model,mitral cell,granule cell,reciprocal synapse,glomerular column

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