title: Topological dynamics in spike-timing dependent plastic model neural networks.
abstract: Spike-timing dependent plasticity (STDP) is a biologically constrained unsupervised form of learning that potentiates or depresses synaptic connections based on the precise timing of pre-synaptic and post-synaptic firings. The effects of on-going STDP on the topology of evolving model neural networks were assessed in 50 unique simulations which modeled 2 h of activity. After a period of stabilization, a number of global and local topological features were monitored periodically to quantify on-going changes in network structure. Global topological features included the total number of remaining synapses, average synaptic strengths, and average number of synapses per neuron (degree). Under a range of different input regimes and initial network configurations, each network maintained a robust and highly stable global structure across time. Local topology was monitored by assessing state changes of all three-neuron subgraphs (triads) present in the networks. Overall counts and the range of triad configurations varied little across the simulations; however, a substantial set of individual triads continued to undergo rapid state changes and revealed a dynamic local topology. In addition, specific small-world properties also fluctuated across time. These findings suggest that on-going STDP provides an efficient means of selecting and maintaining a stable yet flexible network organization.
authors.affiliation: Department of Psychology, University of New Mexico Albuquerque, NM, USA.
authors.forename: David B
authors.initials: DB
authors.lastname: Stone
authors.forename: Claudia D
authors.initials: CD
authors.lastname: Tesche
mesh_terms: D000200:Action Potentials / Q000502:physiology; D006801:Humans; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology; D013997:Time Factors
keywords: graph theory; motif; neural network; simulation; small-world; spike-timing dependent plasticity (STDP); topology