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The Journal of Neuroscience, June 9, 2004, 24(23):5427-5438; doi:10.1523/JNEUROSCI.4449-03.2004
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Behavioral/Systems/Cognitive
Using a Hybrid Neural System to Reveal Regulation of Neuronal Network Activity by an Intrinsic Current
Michael Sorensen,1,2 Stephen DeWeerth,1 Gennady Cymbalyuk,2 andRonald L. Calabrese2 1Laboratory for Neuroengineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0363, and 2Department of Biology, Emory University, Atlanta, Georgia 30322
The generation of rhythmic patterns by neuronal networks is a complex phenomenon, relying on the interaction of numerous intrinsic and synaptic currents, as well as modulatory agents. To investigate the functional contribution of an individual ionic current to rhythmic pattern generation in a network, we constructed a hybrid system composed of a silicon model neuron and a heart interneuron from the heartbeat timing network of the medicinal leech. When the model neuron and a heart interneuron are connected by inhibitory synapses, they produce rhythmic activity similar to that observed in the heartbeat network. We focused our studies on investigating the functional role of the hyperpolarization-activated inward current (Ih) on the rhythmic bursts produced by the network. By introducing changes in both the model and the heart interneuron, we showed that Ih determines both the period of rhythmic bursts and the balance of activity between the two sides of the network, because the amount and the activation/deactivation time constant of Ih determines the length of time that a neuron spends in the inhibited phase of its burst cycle. Moreover, we demonstrated that the model neuron is an effective replacement for a heart interneuron and that changes made in the model can accurately mimic similar changes made in the living system. Finally, we used a previously developed mathematical model (Hill et al. 2001) of two mutually inhibitory interneurons to corroborate these findings. Our results demonstrated that this hybrid system technique is advantageous for investigating neuronal properties that are inaccessible with traditional techniques.
Key words: VLSI; hybrid systems analysis; silicon neurons; leech; h-current; half-center oscillator; model neurons; dynamic clamp
Received Sep 30, 2003; revised May 3, 2004; accepted May 3, 2004.
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