...................................................................... Modeling Projection Neuron and Neuromodulatory Effects on a Rhythmic Neural Network.
Department of Mathematical Sciences
Projection neurons shape the activity of many neural networks. In particular, neuromodulatory substances, which are often released by projection neurons, alter the cellular and/or synaptic properties within a target network. However, neural networks in turn influence projection neuron input via synaptic feedback. This dissertation uses mathematical and biophysically-realistic modeling to investigate these issues in the gastric mill (chewing) motor network of the crab, Cancer borealis. The projection neuron MCN1 elicits a gastric mill rhythm in which the LG neuron and INT1 burst in anti-phase due to their reciprocal inhibition. However, bath application of the neuromodulator PK elicits a similar gastric mill rhythm in the absence of MCN1 participation; yet, the mechanism that underlies the PK-elicited rhythm is unknown. This dissertation develops a 2-dimensional model that is used to propose three potential mechanisms by which PK can elicit a similar gastric mill rhythm. The network dynamics of the MCN1-elicited and PK-elicited rhythms are also compared using geometrical properties in the phase plane. Next, the two gastric mill rhythms are compared using a more biophysically-realistic model. Presynaptic inhibition of MCN1 is necessary for coordinating network activity during the MCN1-elicited rhythm. In contrast, the PK-elicited rhythm is shown to be coordinated by a synapse that is not functional during the MCN1-elicited rhythm.
Last Modified: Jan 18, 2006 Victor Matveev m a t v e e v @ n j i t . e d u |