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      Dopamine maintains network synchrony via direct modulation of gap junctions in the crustacean cardiac ganglion

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          Abstract

          The Large Cell (LC) motor neurons of the crab cardiac ganglion have variable membrane conductance magnitudes even within the same individual, yet produce identical synchronized activity in the intact network. In a previous study we blocked a subset of K + conductances across LCs, resulting in loss of synchronous activity (Lane et al., 2016). In this study, we hypothesized that this same variability of conductances makes LCs vulnerable to desynchronization during neuromodulation. We exposed the LCs to serotonin (5HT) and dopamine (DA) while recording simultaneously from multiple LCs. Both amines had distinct excitatory effects on LC output, but only 5HT caused desynchronized output. We further determined that DA rapidly increased gap junctional conductance. Co-application of both amines induced 5HT-like output, but waveforms remained synchronized. Furthermore, DA prevented desynchronization induced by the K + channel blocker tetraethylammonium (TEA), suggesting that dopaminergic modulation of electrical coupling plays a protective role in maintaining network synchrony.

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          Most cited references58

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          Variability, compensation, and modulation in neurons and circuits.

          Eve Marder (2011)
          I summarize recent computational and experimental work that addresses the inherent variability in the synaptic and intrinsic conductances in normal healthy brains and shows that multiple solutions (sets of parameters) can produce similar circuit performance. I then discuss a number of issues raised by this observation, such as which parameter variations arise from compensatory mechanisms and which reflect insensitivity to those particular parameters. I ask whether networks with different sets of underlying parameters can nonetheless respond reliably to neuromodulation and other global perturbations. At the computational level, I describe a paradigm shift in which it is becoming increasingly common to develop families of models that reflect the variance in the biological data that the models are intended to illuminate rather than single, highly tuned models. On the experimental side, I discuss the inherent limitations of overreliance on mean data and suggest that it is important to look for compensations and correlations among as many system parameters as possible, and between each system parameter and circuit performance. This second paradigm shift will require moving away from measurements of each system component in isolation but should reveal important previously undescribed principles in the organization of complex systems such as brains.
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            Neuromodulation of neurons and synapses.

            Neuromodulation underlies the flexibility of neural circuit operation and behavior. Individual neuromodulators can have divergent actions in a neuron by targeting multiple physiological mechanisms. Conversely, multiple neuromodulators may have convergent actions through overlapping targets. The divergent and convergent neuromodulator actions can be unambiguously synergistic or antagonistic, but neuromodulation often entails balanced adjustment of nonlinear membrane and synaptic properties by targeting ion channel and synaptic dynamics rather than just excitability or synaptic strength. In addition, neuromodulators can exert effects at multiple timescales, from short-term adjustments of neuron and synapse function to persistent long-term regulation. This short review summarizes some highlights of the diverse actions of neuromodulators on ion channel and synaptic properties.
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              Global structure, robustness, and modulation of neuronal models.

              The electrical characteristics of many neurons are remarkably robust in the face of changing internal and external conditions. At the same time, neurons can be highly sensitive to neuromodulators. We find correlates of this dual robustness and sensitivity in a global analysis of the structure of a conductance-based model neuron. We vary the maximal conductance parameters of the model neuron and, for each set of parameters tested, characterize the activity pattern generated by the cell as silent, tonically firing, or bursting. Within the parameter space of the five maximal conductances of the model, we find directions, representing concerted changes in multiple conductances, along which the basic pattern of neural activity does not change. In other directions, relatively small concurrent changes in a few conductances can induce transitions between these activity patterns. The global structure of the conductance-space maps implies that neuromodulators that alter a sensitive set of conductances will have powerful, and possibly state-dependent, effects. Other modulators that may have no direct impact on the activity of the neuron may nevertheless change the effects of such direct modulators via this state dependence. Some of the results and predictions arising from the model studies are replicated and verified in recordings of stomatogastric ganglion neurons using the dynamic clamp.
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                Author and article information

                Contributors
                Role: Reviewing Editor
                Role: Senior Editor
                Journal
                eLife
                Elife
                eLife
                eLife
                eLife Sciences Publications, Ltd
                2050-084X
                16 October 2018
                2018
                : 7
                : e39368
                Affiliations
                [1 ]deptDivision of Biological Sciences University of Missouri ColumbiaUnited States
                [2 ]deptDepartment of Electrical Engineering and Computer Science University of Missouri ColumbiaUnited States
                Emory University United States
                Harvard University United States
                Emory University United States
                Emory University United States
                Miami University United States
                Author notes
                [†]

                Department of Biology, Brandeis University, Waltham, United States.

                Author information
                http://orcid.org/0000-0003-3416-2377
                http://orcid.org/0000-0002-9002-1862
                http://orcid.org/0000-0002-1489-7029
                http://orcid.org/0000-0003-4532-5362
                Article
                39368
                10.7554/eLife.39368
                6199132
                30325308
                5d170d1c-c755-4ebb-8229-dad35da121e6
                © 2018, Lane et al

                This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

                History
                : 25 June 2018
                : 11 October 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: R01MH046742-29
                Award Recipient :
                The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
                Categories
                Research Advance
                Neuroscience
                Custom metadata
                Dopamine is able to ensure that neural networks maintain critical features of their output, such as synchrony of neuron firing, by directly increasing coupling strength to ensure robust output is maintained.

                Life sciences
                cancer borealis,neuromodulation,electrical coupling,gap junction,robustness,other
                Life sciences
                cancer borealis, neuromodulation, electrical coupling, gap junction, robustness, other

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