Author : Xiumin Li
Publisher :
ISBN 13 :
Total Pages : 242 pages
Book Rating : 4.:/5 (754 download)
Book Synopsis Cortical Oscillations and Synaptic Plasticity by : Xiumin Li
Download or read book Cortical Oscillations and Synaptic Plasticity written by Xiumin Li and published by . This book was released on 2011 with total page 242 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 which exhibit different degrees of excitability, the network finally evolves into a sparse and active-neuron-dominant structure. That is, strong connections are mainly distributed to the synapses from active neuronsto inactive ones. We argue that this self-emergent topology essentially reflects the competition of different neurons and encodes the heterogeneity. This structure is shown to significantly promote synchronization and enhance the coherence resonance and stochastic resonance of the entire network, indicating its high efficiency in information processing. Based on this work, we further develop another network organized from two stages of learning process, including STDP and another burst-based plasticity, i.e., burst-timing dependent plasticity (BTDP). The likely relationship between the learning rules with different timescales and the formation of architecture with different special scales is explored. The final network exhibits a two-level hierarchical structure after the synaptic refinement. This self-organized network shows higher sensitivity to afferent current injection compared with alternative archetypal networks with different neural connectivity. Statistical analysis also demonstrates that 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 in the neural network.