There is a significant amount of computational literature on networks of neurons and their resulting behavior. This dissertation combines electrophysiology experiments with computational modeling to validate the assumptions and results found in this literature. First, we investigate the weak coupling assumption, which states that the phase response of a neuron to weak stimuli is separable from the stimulus waveform. For weak stimuli, there is an intrinsic neuronal property described by the infinitesimal phase response curve (IPRC) that will predict the phase response when convolved with the stimulus waveform. Here, we show that there is a linear relationship between the stimulus and phase response of the neuron, and that we are able to obtain IPRCs that successfully predict the neuronal phase response. Next, we use hybrid networks of neurons to study the phase locking behavior of networks as the synaptic time constant is changed. We verify that networks show anti-phase synchrony for fast time constants, and in-phase synchrony for slow time constants. We also show that phase models and phase response curves (PRCs) qualitatively predict phase locking observed in electrophysiology experiments. Finally, we investigate the stability of the dynamic clamp system. We determined that the maximal conductance of the current being simulated, the dynamic clamp sampling rate, the amount of electrode resistance compensation, and the amount of capacitance compensation all affect when the instability is present. There is a dramatic increase in stability when the electrode resistance and system capacitance are well compensated.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/24799 |
| Date | 09 July 2007 |
| Creators | Preyer, Amanda Jervis |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Detected Language | English |
| Type | Dissertation |
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