Conductance Ratios and Cellular Identity

Hudson, Amber E.; Prinz, Astrid A.

doi:10.1371/journal.pcbi.1000838

Cited by 81 publications

(95 citation statements)

References 39 publications

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“…Experimental and computational studies suggest that distinct conductance ratios are maintained in identified pyloric neurons, including the ratio of LP I A to I h (Schulz et al, 2006(Schulz et al, , 2007Hudson and Prinz, 2010), and modulators are necessary for their maintenance (Khorkova and Golowasch, 2007). Interestingly, steadystate DA also regulates LP I A G max (Rodgers et al, 2011).…”

Section: Discussionmentioning

confidence: 99%

Tonic Nanomolar Dopamine Enables an Activity-Dependent Phase Recovery Mechanism That Persistently Alters the Maximal Conductance of the Hyperpolarization-Activated Current in a Rhythmically Active Neuron

Rodgers

Fu²,

Krenz³

et al. 2011

J. Neurosci.

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The phases at which network neurons fire in rhythmic motor outputs are critically important for the proper generation of motor behaviors. The pyloric network in the crustacean stomatogastric ganglion generates a rhythmic motor output wherein neuronal phase relationships are remarkably invariant across individuals and throughout lifetimes. The mechanisms for maintaining these robust phase relationships over the long-term are not well described. Here we show that tonic nanomolar dopamine (DA) acts at type 1 DA receptors (D1Rs) to enable an activity-dependent mechanism that can contribute to phase maintenance in the lateral pyloric (LP) neuron. The LP displays continuous rhythmic bursting. The activity-dependent mechanism was triggered by a prolonged decrease in LP burst duration, and it generated a persistent increase in the maximal conductance (G max ) of the LP hyperpolarization-activated current (I h ), but only in the presence of steady-state DA. Interestingly, micromolar DA produces an LP phase advance accompanied by a decrease in LP burst duration that abolishes normal LP network function. During a 1 h application of micromolar DA, LP phase recovered over tens of minutes because, the activity-dependent mechanism enabled by steady-state DA was triggered by the micromolar DA-induced decrease in LP burst duration. Presumably, this mechanism restored normal LP network function. These data suggest steady-state DA may enable homeostatic mechanisms that maintain motor network output during protracted neuromodulation. This DA-enabled, activitydependent mechanism to preserve phase may be broadly relevant, as diminished dopaminergic tone has recently been shown to reduce I h in rhythmically active neurons in the mammalian brain.

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Section: Discussionmentioning

confidence: 99%

Tonic Nanomolar Dopamine Enables an Activity-Dependent Phase Recovery Mechanism That Persistently Alters the Maximal Conductance of the Hyperpolarization-Activated Current in a Rhythmically Active Neuron

Rodgers

Fu²,

Krenz³

et al. 2011

J. Neurosci.

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“…Modeling work in the pyloric network of the crustacean STN has suggested that, rather than connection strengths, it may be their ratios that are important in network function (Hudson and Prinz 2010). The importance of synaptic ratios is supported by the data presented here, where there is substantial variability in connection strengths among individuals, but there do appear to be some points (that we can measure and accurately compare) where the balance (ratio) of the two synapses appears to be maintained as indicated by significant correlations [for example, the HN(2) to HN(4) connection compared with the HN(4) to HN(4) connection or the HN(5) to HN(7i) connection compared with the HN(3) to HN(7) connection (Figs.…”

Section: Discussionmentioning

confidence: 99%

Animal-to-animal variability of connection strength in the leech heartbeat central pattern generator

Roffman

Norris

Calabrese

2012

Journal of Neurophysiology

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Roffman RC, Norris BJ, Calabrese RL. Animal-to-animal variability of connection strength in the leech heartbeat central pattern generator. J Neurophysiol 107: 1681-1693, 2012. First published December 21, 2011 doi:10.1152/jn.00903.2011.-The heartbeat central pattern generator (CPG) in medicinal leeches controls blood flow within a closed circulatory by programming the constrictions of two parallel heart tubes. This circuit reliably produces a stereotyped fictive pattern of activity and has been extensively characterized. Here we determined, as quantitatively as possible, the strength of each inhibitory synapse and electrical junction within the core circuit of the heartbeat CPG. We also examined the animal-to-animal variability in strengths of these connections and, for some, determined the correlations between connections to the same postsynaptic target. The core CPG is composed of seven bilateral pairs of heart interneurons connected via both inhibitory chemical synapses and electrical junctions. Fifteen different connections within the core CPG were measured for strength using extracellular presynaptic recordings and postsynaptic voltage-clamp recordings across a minimum of seven individuals each, and the animal-to-animal variability was characterized. Connection strengths within the core network varied three to more than sevenfold among individuals (depending on the specific connection). The balance between two inputs onto various postsynaptic targets was explored by within-individual comparisons and correlation across individuals. Of the seven comparisons made within the core CPG, three showed a clear correlation of connection strengths, while the other four did not. We conclude that the leech heartbeat CPG can withstand wide variability in connection strengths and still produce stereotyped output. The network appears to preserve the relative strengths of some pairs of inputs, despite the animal-toanimal variability. central pattern generator; neuronal networks; spike-triggered average FOR NEURONAL NETWORKS TO RELIABLY process sensory information or program motor output, they must produce reliable, albeit plastic, output. Over the past decade, we have come to realize that reliable network output can result from networks in which the intrinsic membrane properties (maximal conductances) of the neurons and the strengths of the synaptic connections can show two-to fivefold animal-to-animal variability (Goaillard et al.

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“…This puzzle was resolved by the finding that the overexpression of I A was accompanied by a compensating up-regulation of the hyperpolarization-activated inward current (I H ) (38,41). Interestingly, strong correlations in the expression of I A and I H mRNA are also seen in crab STG neurons (11,20,25), and it has been suggested that the specific patterns of correlations seen in identified neurons may be a signature of their identity (25,26,42,43). …”

Section: Parameter Compensation and Correlationsmentioning

confidence: 99%

Variability, compensation, and modulation in neurons and circuits

Marder

2011

Proc. Natl. Acad. Sci. U.S.A.

341

370

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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.circuit dynamics | neuronal variability | neuronal homeostasis | dynamic clamp A ll experimental biologists face a daily conundrum: on the one hand, we know that all individual biological organisms, be they lobsters, cats, or humans, are distinct individuals. On the other hand, we must do experiments on multiple individuals to ensure the reliability of our results. As biologists wishing to understand how the function of a cell, a circuit, or a brain depends on the properties of its constituent processes, we confront two issues: (i) all our data come with associated measurement error (often difficult to assess), and (ii) there is considerable natural variability in the populations we study. Consequently, we conventionally rely on statistics calculated from populations to assure ourselves that our measurements are reliable. Most commonly, we report mean data, with the underlying assumption that these means capture something akin to a "platonic ideal" of the individual neurons or animals whose properties were measured. Although this strategy has been enormously useful over the years, it has many limitations, some of which I discuss below. Multiple Solutions and Failure of AveragingDespite the widespread use of means, computational work has shown unambiguously some of the dangers and confounds that can come from exclusive reliance on mean data (1-3). One example of this comes from a study in which several thousand model neurons were generated by randomly picking the maximal conductances of the five different ionic conductances in the model (2). Fig. 1 shows three examples of single-spike bursters chosen from a population of 164 single-spike bursters generated in this study. Alth...

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Conductance Ratios and Cellular Identity

Cited by 81 publications

References 39 publications

Tonic Nanomolar Dopamine Enables an Activity-Dependent Phase Recovery Mechanism That Persistently Alters the Maximal Conductance of the Hyperpolarization-Activated Current in a Rhythmically Active Neuron

Tonic Nanomolar Dopamine Enables an Activity-Dependent Phase Recovery Mechanism That Persistently Alters the Maximal Conductance of the Hyperpolarization-Activated Current in a Rhythmically Active Neuron

Animal-to-animal variability of connection strength in the leech heartbeat central pattern generator

Variability, compensation, and modulation in neurons and circuits

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