THE STUDY OF NEURONAL SYNCHRONY USING COUPLED OSCILLATOR MODEL FOR BRAIN FUNCTIO

这个子项目是众多研究子项目之一

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Brain function restoration therapy is on the frontier of biomedical research in which the interface between computer models and neurons is created to compensate for cognitive loss due to neurological disorders such as Alzheimer's, trauma or dementia. The existing approach utilizes kernel estimation based on input-output white-noise system identification has two major shortcomings. Some hippocampal sub-regions responsible for memory consolidation exhibit intrinsic oscillatory behavior and may not be suitably represented using a kernel method. Second, it lacks the capability for adaptive synaptic characteristics. Here, we propose the use of coupled oscillators. It is capable of modeling neurons as either autonomous or threshold-driven oscillators connected through biologically-relevant coupling factors. We hypothesize that this model is more suitable for the modeling of the hippocampal regions with strong oscillatory behavior and has a great potential for the cognitive neuroprosthetic application. In this project, we investigate the utility of noisy input as a way to enhance neuronal synchronization at selective frequencies. The simulated waveforms are compared with the biological data using state space reconstruction and clustering techniques. Our preliminary result indicated that the detection of subthreshold rhythmic activities in frequency ranges associated with learning and higher cognitive functions can be enhanced with a small and appropriate range of electric field noise. Our group is currently investigating the effect of noise on pulse train reproducibility and the optimization techniques to select the appropriate receptor parameters based on the computer simulation and in vitro experimental validation.