Peacock Scholarship

A Mathematical Model to Study the Influence of Modulatory Input on a Rhythmic Neural Network

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Input from neuromodulators shapes the rhythmic output of a central pattern generator (CPG) network. When released by a projection neuron, the impact of neuromodulators is convoluted by synaptic interactions with a target network. Yet, how synaptic interactions influence neuromodulation of target networks is not well understood. We use a mathematical model to examine this issue in the gastric mill CPG of the crab, Cancer borealis. Physiologically, the projection neuron MCN1 elicits a gastric mill rhythm (GMR) via synaptic excitation of the LG neuron. This GMR is represented by the biphasic activity pattern of the lateral gastric (LG) neuron. Many previous models have treated MCN1 action on LG as a slow, passive current. However, MCN1 also triggers a modulator-activated, voltage-gated inward current (IMI) in LG. We examine the influence of IMI in our model. We show that IMI primarily impacts one phase of LG neuron activity. Next, we show that IMI produces a similar influence on LG activity as that of a core inhibitory synapse from Interneuron 1 (INT1) onto LG. Finally, we show that removal of the INT1-to-LG synapse disrupts the GMR, but the GMR activity can be restored by modifying the properties of IMI.We conclude that synaptic interactions can enable neuromodulators to provide a CPG with more flexibility for producing rhythmic output.

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  • 10/23/2019
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