This study uses a conductance-based computer simulation to test the feasibility of a mechanism underlying a newly-described dynamic form of neuromodulation, called spike-timing dependent neuromodulation (STDN). In the mollusc, Tritonia diomedea, it was recently found that a serotonergic neuron (called DSI) alters the synaptic strength of another neuron (VSI-B) in a temporally biphasic-bidirectional manner, with an initial potentiation followed by prolonged synaptic depression (Sakurai and Katz 2003). Physiological evidence suggested that the depression phase is due to serotonin enhancing the A-current in VSI-B, thereby causing spike-narrowing or a decrease in spike amplitude, and thus a decrease in transmitter release. We sought to test the feasibility of this mechanism by developing a conductance-based model of VSI-B using a Hodgkin-Huxley style simulation with a minimal number of ion conductances: A-current, delayed rectifier potassium, fast sodium, and leak channels.
From our model, we conducted simulations in order to study how the spike shape of the VSI-B action potential changes as the A-current conductance is enhanced, from which we are able to predict the amount of depression in the post-synaptic cell. Our model indicates that the depression due to the narrowing of the spike with A-current enhancement is sufficient to account for the empirically observed depression during STDN, although it suggests a greater effect of serotonin at the terminals than is observed in the soma. Additionally, the model suggested that the slow inactivation kinetics of the A-current cannot explain the dynamics of the depression phase of STDN. These modeling results suggest that serotonergic modulation of the A-current plays a role in STDN but does not account for its dynamics.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/7257 |
| Date | 18 May 2004 |
| Creators | Darghouth, Naim Richard |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | 2721255 bytes, application/pdf |
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