The goal of this thesis was to use two-cell circuits to gain a better understanding of how variable intrinsic and synaptic properties influence neuronal circuit activity. Using the stomatogastric ganglion (STG) of the crab C. borealis and the dynamic clamp, I studied how two artificial conductances could compensate for intrinsic variability to produce similar network output. I also examined whether a neuromodulator that changes intrinsic excitability would have consistent effects on circuit output in a variety of network configurations.

Studies have shown that similar output can be elicited from neuronal networks that have different underlying structure. To determine whether two opposing conductances could compensate for intrinsic neuronal variability, I used the dynamic clamp to simulate an artificial excitatory membrane conductance (g_h) and an inhibitory synaptic conductance (g_syn) in hybrid networks built with an STG neuron coupled to a model neuron. Through systematic searches of the g_syn and g_h conductance space, I found that every neuron was capable of producing a cell type-specific target activity. The dynamic clamp's ability to simulate and measure compensatory currents provided direct insight into the many ways that diverse circuits can be tuned to produce equivalent output.

I then studied whether a neuromodulator that increased the intrinsic excitability of gastric mill neurons of the STG had consistent effects on the output of a circuit formed from these cells using the dynamic clamp. I found that overall, both serotonin and oxotremorine each produced statistically significant increases in burst frequency in two-cell circuits with diverse network architectures. However, there were also a small number of anomalous networks that slowed in the presence of these excitatory modulators, indicating that it is difficult to predict how network structure will interact with endogenous membrane conductances and produce the opposite of the expected response.

Artificial two-cell circuits serve as a practical tool for studying the implications of variability at the cellular and network level. This variability provides neurons and neuronal networks a multitude of ways to produce reliable yet flexible output in different neuromodulatory environments.