The complexity of small circuits: the stomatogastric nervous system

Daur, Nelly; Nadim, Farzan; Bucher, Dirk

doi:10.1016/j.conb.2016.07.005

Cited by 71 publications

(66 citation statements)

References 69 publications

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“…The activity of neural circuits is flexible, and much of this flexibility is provided by modulatory transmitters and hormones which alter synaptic and intrinsic neuronal properties (Brezina, 2010; Harris-Warrick, 2011; Jordan and Slawinska, 2011; Bargmann, 2012; Marder, 2012; Bucher and Marder, 2013; Nadim and Bucher, 2014). The pyloric circuit is sensitive to a plethora of small molecule transmitters and neuropeptides which affect cycle frequency and phase relationships (Marder and Bucher, 2007; Stein, 2009; Daur et al, 2016). With respect to our findings, any given neuromodulator could act presynaptically to alter P , duration, or duty cycle on the one hand, and g max and Δ peak on the other.…”

Section: Discussionmentioning

confidence: 99%

“…The pyloric circuit generates a triphasic motor pattern with stable phase relationships over a wide range of periods (Eisen and Marder, 1984; Hooper, 1997b, a; Bucher et al, 2005; Goaillard et al, 2009; Tang et al, 2012; Soofi et al, 2014). Follower neurons burst in rebound from inhibition from pacemaker neurons (Marder and Bucher, 2007; Daur et al, 2016), and post-inhibitory rebound delay scales with the period of hyperpolarizing currents (Hooper, 1998). Voltage-gated conductances slow enough for cumulative activation across cycles could promote such phase maintenance (Hooper et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

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Short-Term Synaptic Dynamics Control the Activity Phase of Neurons in an Oscillatory Network

Martínez

Anwar

Bose

et al. 2019

Preprint

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19In oscillatory systems, neuronal activity phase is often independent of network 20 frequency. Such phase maintenance requires adjustment of synaptic input with 21 network frequency, a relationship that we explored using the crab, Cancer borealis, 22 pyloric network. The burst phase of pyloric neurons is relatively constant despite a >2-23 fold variation in network frequency. We used noise input to characterize how input 24 shape influences burst delay of a pyloric neuron, and then used dynamic clamp to 25 examine how burst phase depends on the period, amplitude, duration, and shape of 26 rhythmic synaptic input. Phase constancy across a range of periods required a 27 proportional increase of synaptic duration with period. However, phase maintenance 28 was also promoted by an increase of amplitude and peak phase of synaptic input with 29 period. Mathematical analysis shows how short-term synaptic plasticity can 30 coordinately change amplitude and peak phase to maximize the range of periods over 31 which phase constancy is achieved. 32 150/150 33

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-Term Synaptic Dynamics Control the Activity Phase of Neurons in an Oscillatory Network

Martínez

Anwar

Bose

et al. 2019

Preprint

Self Cite

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“…In terms of neurological studies, in vitro whole-cell recordings are the most common, although in vivo live-animal recordings, which are inherently more difficult, are becoming more refined (Scanziani and Häusser, 2009). Crustacean model systems have been used heavily for electrophysiological studies (Daur et al, 2016;Dickinson et al, 2016;Otopalik et al, 2017). For example, the effects of neuromodulators on the same neuronal circuit was explored for the Jonah crab gastric mill motor pattern, which was interestingly explained by using a mathematical model (Kintos et al, 2016).…”

Section: Altering Neuropeptide Contentmentioning

confidence: 99%

New techniques, applications and perspectives in neuropeptide research

DeLaney

Buchberger

Atkinson

et al. 2018

Journal of Experimental Biology

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Neuropeptides are one of the most diverse classes of signaling molecules and have attracted great interest over the years owing to their roles in regulation of a wide range of physiological processes. However, there are unique challenges associated with neuropeptide studies stemming from the highly variable molecular sizes of the peptides, low concentrations, high degree of structural diversity and large number of isoforms. As a result, much effort has been focused on developing new techniques for studying neuropeptides, as well as novel applications directed towards learning more about these endogenous peptides. The areas of importance for neuropeptide studies include structure, localization within tissues, interaction with their receptors, including ion channels, and physiological function. Here, we discuss these aspects and the associated techniques, focusing on technologies that have demonstrated potential in advancing the field in recent years. Most identification and structural information has been gained by mass spectrometry, either alone or with confirmations from other techniques, such as nuclear magnetic resonance spectroscopy and other spectroscopic tools. While mass spectrometry and bioinformatic tools have proven to be the most powerful for large-scale analyses, they still rely heavily on complementary methods for confirmation. Localization within tissues, for example, can be probed by mass spectrometry imaging, immunohistochemistry and radioimmunoassays. Functional information has been gained primarily from behavioral studies coupled with tissue-specific assays, electrophysiology, mass spectrometry and optogenetic tools. Concerning the receptors for neuropeptides, the discovery of ion channels that are directly gated by neuropeptides opens up the possibility of developing a new generation of tools for neuroscience, which could be used to monitor neuropeptide release or to specifically change the membrane potential of neurons. It is expected that future neuropeptide research will involve the integration of complementary bioanalytical technologies and functional assays.

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“…Neural networks must be capable of producing output that is robust and reliable, yet also flexible enough to meet changing environmental demands. One mechanism of providing flexibility to network activity is neuromodulation, which reconfigures network output by altering a subset of cellular and synaptic conductances ( Harris-Warrick, 2011; Bargmann, 2012; Daur et al, 2016 ). However, many networks achieve stable output by a variety of solutions; intrinsic membrane conductances and synaptic strengths can be highly variable yet still produce nearly identical physiological activity ( Ball et al, 2010; Calabrese et al, 2011; Marder, 2011; Ransdell et al, 2013b ).…”

Section: Introductionmentioning

confidence: 99%

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

Lane

Kick

Wilson

et al. 2018

Preprint

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Dopamine maintains network1 synchrony via direct modulation of 2 gap junctions in the crustacean 3 cardiac ganglion Abstract The Large Cell (LC) motor neurons of the crab (C. borealis) cardiac ganglion have 9variable membrane conductance magnitudes even within the same individual, yet produce 10 identical synchronized activity in the intact network. In our previous study (Lane et al., 2016) we 11 blocked a subset of K + conductances across LCs, resulting in loss of synchronous activity. In this 12 study, we hypothesized that this same variability of conductances could make LCs vulnerable to 13 desynchronization during neuromodulation. We exposed the LCs to serotonin (5HT) and dopamine 14 (DA) while recording simultaneously from multiple LCs. Both amines had distinct excitatory effects 15 on LC output, but only 5HT caused desynchronized output. We further determined that DA rapidly 16 increased gap junctional conductance. Co-application of both amines induced 5HT-like output, but 17 waveforms remained synchronized. Furthermore, DA prevented desynchronization induced by the 18 K+ channel blocker tetraethylammonium (TEA), suggesting that dopaminergic modulation of 19 electrical coupling plays a protective role in maintaining network synchrony. 20 21 22Neural networks must be capable of producing output that is robust and reliable, yet also flexible 23 enough to meet changing environmental demands. One mechanism of providing flexibility to 24 network activity is neuromodulation, which reconfigures network output by altering a subset 25 of cellular and synaptic conductances (Harris-Warrick, 2011; Bargmann, 2012; Daur et al., 2016). 26 However, many networks achieve stable output by a variety of solutions; intrinsic membrane 27 conductances and synaptic strengths can be highly variable yet still produce nearly identical 28 physiological activity (Ball et al., 2010; Calabrese et al., 2011; Marder, 2011; Ransdell et al., 2013b). 29 This raises a fundamental question about neuromodulation, highlighted in a recent review by 30 Marder et al. (2014), as to whether modulation of networks with variable underlying parameters 31 can produce predictable and reliable results. These authors demonstrate computationally that 32 modulation of neurons with similar outputs arising from variable underlying conductances can 33 cause anywhere from relatively small to fairly substantial differences in output (Marder et al., 2014). 34 Therefore, the response of any neural network to modulation is likely state-dependent (Goldman 35 et al., 2001; Nadim et al., 2008; Gutierrez and Marder, 2014; Williams et al., 2013; Marder et al., 36 2014), and potentially unpredictable as a result of these varying underlying conductances (Marder 37 et al., 2014). In some cases neuromodulation can expand the parameter space in which a given 38 activity feature is maintained (Grashow et al., 2009), potentially leading to protective effects of 39 1 of 18 216 Both 5HT and DA have been extensively studied in crustacean motor neurons, particularly those of 2...

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The complexity of small circuits: the stomatogastric nervous system

Cited by 71 publications

References 69 publications

Short-Term Synaptic Dynamics Control the Activity Phase of Neurons in an Oscillatory Network

Short-Term Synaptic Dynamics Control the Activity Phase of Neurons in an Oscillatory Network

New techniques, applications and perspectives in neuropeptide research

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

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