title: Emergence of small-world structure in networks of spiking neurons through STDP plasticity.
abstract: In this work, we use a complex network approach to investigate how a neural network structure changes under synaptic plasticity. In particular, we consider a network of conductance-based, single-compartment integrate-and-fire excitatory and inhibitory neurons. Initially the neurons are connected randomly with uniformly distributed synaptic weights. The weights of excitatory connections can be strengthened or weakened during spiking activity by the mechanism known as spike-timing-dependent plasticity (STDP). We extract a binary directed connection matrix by thresholding the weights of the excitatory connections at every simulation step and calculate its major topological characteristics such as the network clustering coefficient, characteristic path length and small-world index. We numerically demonstrate that, under certain conditions, a nontrivial small-world structure can emerge from a random initial network subject to STDP learning.
authors: Centre for Robotics and Neural Systems (CRNS), University of Plymouth, Plymouth, PL4 8AA, UK. gbasalyga@plymouth.ac.uk
authors: Gleb
authors: G
authors: Basalyga
authors: Pablo M
authors: PM
authors: Gleiser
authors: Thomas
authors: T
authors: Wennekers
mesh_terms: D000200:Action Potentials; D008962:Models, Theoretical; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology*