Cortical oscillations and synaptic plasticity --- from a single neuron to neural networks

Cortical oscillations have been widely observed in the brain cortex and play a crucial role for understanding temporal correlations in cortical circuits. Synchronous activities of neuronal populations, which produce cortical oscillations, have close relationship with synaptic connectivity of neural networks. In this thesis, two kinds of cortical rhythms --- beta oscillation (10∼30 Hz) and gamma oscillation (30∼80 Hz) --- together with the evolution of neural network by synaptic plasticity are investigated.;Firstly, an optimal release from the impact of inhibitory inputs oscillating around beta frequency is observed at the tuft dendrites of a biologically detailed layer V pyramidal neuron model. This preferred frequency range for the maximal firing rate is free from the influence of many parameters. However, removing the hyperpolarization-activated current eliminated this resonance. There is also a critical dependence of resonant frequency on the existence of bursts. Resonant frequency could be remarkably shifted by varying the degree of synchronization of pre-synaptic spikes and the conductance of calcium channels, which are curial for the generation of bursts.;Secondly, the generation of gamma oscillations in a fast-spiking interneuron network is investigated. Simulations show that gap junctions remarkably improve the robustness of synchrony against heterogeneity. We reproduce the experimentally observed regulation of gamma inhibition on pyramidal neurons' response. Besides, synaptic delays have substantial effect on the interactions of this interneuron network and a single pyramidal neuron.;Finally, in order to understand how synchronous activity emerges from self-organized neural networks, we propose a novel network refined from spike-timing dependent plasticity (STDP). Due to the existence of heterogeneity in neurons, the network evolves into a sparse and active-neuron-dominant structure, which essentially reflects the competition between neurons and promotes synchronization. Based on this work, we further develop another network organized from two stages of learning process, including STDP and the burst-timing dependent plasticity (BTDP). After synaptic refinement the network exhibits a two-level hierarchical structure and has high sensitivity to afferent current injection. It has the small-world properties of small shortest path length and high clustering coefficient. Thus the selectively refined connectivity enhances the ability of neuronal communications and improves the efficiency of signal transmission.