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.
Highlights
Neural networks must be capable of producing output that is robust and reliable, yet flexible enough to meet changing environmental demands
5HT and DA have distinct excitatory effects when applied to the entire network. Both 5HT (10À6M) and DA (10À5M) are excitatory when applied to the entire cardiac ganglion (CG) of C. borealis (Cruz-Bermudez and Marder, 2007), and our results recapitulate this same effect (Figure 2). 5HT significantly increased pacemaker burst duration, and in 6 out of 8 experiments switched the network to a distinct output consisting of a single prolonged pacemaker burst driving two different and distinct Large Cell (LC) bursts that we term ‘double-bursting’ (Figure 2A)
Because this represents a distinct mode of firing from control, direct comparisons of LC burst characteristics between these two modes of firing did not seem appropriate
Summary
Neural networks must be capable of producing output that is robust and reliable, yet flexible enough to meet changing environmental demands. 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, 2013a). This raises a fundamental question about neuromodulation, highlighted in a recent review by Marder et al (2014), as to whether modulation of networks with variable underlying parameters can produce predictable and reliable results. These questions have never been addressed in a network that relies on synchronous activity for appropriate physiological output
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