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Oscillations and Spiking Pairs: Behavior of a Neuronal Model with STDP Learning

victorshenshx{at}gmail.com Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2BT, U.K.

Xiaobin Lin

XL61{at}hw.ac.uk Department of Computer Science, Heriot-Watt University, Edinburgh EH14 4AS, U.K.

Philippe De Wilde

pdw{at}macs.hw.ac.uk Department of Computer Science, Heriot-Watt University, Edinburgh EH14 4AS, U.K.

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.