Hybrid neural-microelectronic systems, systems composed of biological neural networks
and neuronal models, have great potential for the treatment of neural injury and
disease. The utility of such systems will be ultimately determined by the ability of the engineered
component to correctly replicate the function of biological neural networks. These
models can take the form of mechanistic models, which reproduce neural function by describing
the physiologic mechanisms that produce neural activity, and empirical models,
which reproduce neural function through more simplified mathematical expressions.
We present our research into the role of model complexity in creating robust and flexible
behaviors in hybrid systems. Beginning with a complex mechanistic model of a leech
heartbeat interneuron, we create a series of three systematically reduced models that incorporate
both mechanistic and empirical components. We then evaluate the robustness
of these models to parameter variation, and assess the flexibility of the models activities.
The modeling studies are validated by incorporating both mechanistic and semi-empirical
models in hybrid systems with a living leech heartbeat interneuron. Our results indicate
that model complexity serves to increase both the robustness of the system and the ability
of the system to produce flexible outputs.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/6908 |
| Date | 15 April 2005 |
| Creators | Sorensen, Michael Elliott |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Dissertation |
| Format | 3816833 bytes, application/pdf |
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