title: Oscillations and spiking pairs: behavior of a neuronal model with STDP learning.
abstract: In a biologically plausible but computationally simplified integrate-and-fire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models. The effects of spike-timing-dependent plasticity have been known to play a distinct role in information processing in the central nervous system for several years. In this letter, neuronal models with dynamical synapses are constructed, and we analyze the effect of STDP on collective network behavior, such as oscillatory activity, weight distribution, and spike timing precision. We comment on how information is encoded by the neuronal signaling, when synchrony groups may appear, and what could contribute to the uncertainty in decision making.
authors.affiliation: Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, UK. victorshenshx@gmail.com
authors.forename: Xi
authors.initials: X
authors.lastname: Shen
authors.forename: Xiaobin
authors.initials: X
authors.lastname: Lin
authors.forename: Philippe
authors.initials: P
authors.lastname: De Wilde
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D000465:Algorithms; D001185:Artificial Intelligence; D001683:Biological Clocks / Q000502:physiology*; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology; D013997:Time Factors