Psychology ETDs
Publication Date
1-31-2013
Abstract
The effects of continual spike-timing dependent plasticity (STDP) on the topology of evolving neural networks were assessed. After a period of stabilization, a number of topological features were monitored periodically throughout simulations of network activity to quantify changes in network structure. Under a range of different input regimes and initial network configurations, each network maintained a robust and highly stable global structure. At the same time, a substantial set of small three-neuron subgraphs (triads) continued to undergo an array of changes and revealed a dynamic local topology. These findings suggest that on-going STDP provides an efficient means of selecting and maintaining a stable yet flexible network organization.
Degree Name
Psychology
Level of Degree
Doctoral
Department Name
Psychology
First Committee Member (Chair)
Caudell, Thomas
Second Committee Member
Clark, Vincent
Third Committee Member
Hamilton, Derek
Language
English
Keywords
Neural networks (Neurobiology), Neuroplasticity, Self-organizing systems.
Document Type
Dissertation
Recommended Citation
Stone, David. "Topological dynamics of spike-timing dependent plastic neural networks." (2013). https://digitalrepository.unm.edu/psy_etds/136
