Honors Thesis Archive
|Title||Influencing Dynamics in Neural Networks|
|Advisors||John Beggs (Indiana University) and Dan Fleisch|
|Full Text||View Thesis (613 KB) Note: This is a very large file; it may be easier to download the file to your computer and open it from there. |
At the author's request, an electronic copy of this thesis is only available to on-campus users.
|Abstract||Experimental readings from rat cortex in our lab have demonstrated a robust ability of neural networks to maintain critical point dynamics. Whether that ability stems from the activity of a regulatory system or is an inherent property of the network, however, remains unknown. Throughout our investigations, a computational model has served both as a useful diagnostic tool in developing measurements of dynamics and as a predictor of manipulations to be investigated in tissue samples. It demonstrates a robustness similar to the biological samples, suggesting that the network structure alone may be sufficient to maintain stable dynamics. Using the model to explore network parameters, we have identified the distribution of connection strengths between nodes in the network as having a clear influence on the dynamical behavior of the system, and further, we have explained deviations from that relationship by identifying particular connection patterns that link attractive behavior with an inverse branching structure.|