Topological dynamics in spike-timing dependent plastic model neural networks

Front Neural Circuits. 2013 Apr 18;7:70. doi: 10.3389/fncir.2013.00070. eCollection 2013.

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.

Keywords: graph theory; motif; neural network; simulation; small-world; spike-timing dependent plasticity (STDP); topology.

MeSH terms

  • Action Potentials* / physiology
  • Humans
  • Neural Networks, Computer*
  • Neuronal Plasticity* / physiology
  • Time Factors