Functional Consequences of Model Complexity in Hybrid Neural-Microelectronic Systems

Neural modeling;Hybrid systems;Model reduction

Sorensen, Michael Elliott ; Biomedical Engineering

University:Georgia Institute of Technology

Department:Biomedical Engineering

关键词: Neural modeling;    Hybrid systems;    Model reduction;   

Others  :  https://smartech.gatech.edu/bitstream/1853/6908/1/sorensen_michael_e_200505_phd.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Hybrid neural-microelectronic systems, systems composed of biological neural networksand neuronal models, have great potential for the treatment of neural injury anddisease. The utility of such systems will be ultimately determined by the ability of the engineeredcomponent to correctly replicate the function of biological neural networks. Thesemodels can take the form of mechanistic models, which reproduce neural function by describingthe 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 flexiblebehaviors in hybrid systems. Beginning with a complex mechanistic model of a leechheartbeat interneuron, we create a series of three systematically reduced models that incorporateboth mechanistic and empirical components. We then evaluate the robustnessof these models to parameter variation, and assess the flexibility of the models activities.The modeling studies are validated by incorporating both mechanistic and semi-empiricalmodels in hybrid systems with a living leech heartbeat interneuron. Our results indicatethat model complexity serves to increase both the robustness of the system and the abilityof the system to produce flexible outputs.