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研究生： You Anni
Anis Yuniati
論文名稱： Inter-layer interactions of noise-driven neural network
Inter-layer interactions of noise-driven neural network
指導教授： 陳啟明
Chen, Chi-Ming
學位類別： 博士
Doctor
系所名稱： 物理學系
Department of Physics
論文出版年： 2017
畢業學年度： 106
語文別： 英文
論文頁數： 115
中文關鍵詞： biological neural networks 、inter-layer interactions 、noise-driven synchronization 、spike-timing-dependent plasticity 、synchronous firing 、computer simulation 、developing neural networks 、repair mechanism of neural networks
英文關鍵詞： biological neural networks, inter-layer interactions, noise-driven synchronization, spike-timing-dependent plasticity, synchronous firing, computer simulation, developing neural networks, repair mechanism of neural networks
DOI URL： http://doi.org/10.6345/DIS.NTNU.DP.002.2018.B04
論文種類： 學術論文
相關次數： 點閱：127  下載：0 
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    • The role of neurons in the brain as an information processing system has attracted considerable attentions and motivated the development of various research techniques. Computationally simulating the interaction among neurons is very useful in investigating the complexity of a neural network. Developed neural networks may exhibit complex dynamic behaviors, such as synchronization and clustered synchronous firing. In this study, we used the Hodgkin-Huxley (HH) model of neurons to investigate the phase diagram of a developing single-layer neural network and that of a network consisting of two weakly coupled neural layers. These networks are noise driven and learn through the spike-timing-dependent plasticity (STDP) or the inverse STDP rules. We described how these networks transited from a nonsynchronous background activity state (BAS) to a synchronous firing state (SFS) by varying the network connectivity and the learning efficacy. In particular, we studied the interaction between a SFS layer and a BAS layer, and investigated how synchronous firing dynamics was induced in the BAS layer. We further investigated the effect of the inter-layer interaction on a BAS to SFS repair mechanism by considering three types of neuron positioning (random, uniform, and lognormal distributions) and two types of inter-layer connections (random and preferential connections). Among these scenarios, we concluded that the repair mechanism has the largest effect for a network with the lognormal neuron positioning and the preferential inter-layer connections.

    Contents Abstract 2 Declaration 3 Acknowledgement 4 Contents 5 List of figures 8 List of tables 12 List of symbols 13 Chapter 1. Introduction 15 Chapter 2. The brain and nervous system 20 2.1. The neuron 20 2.1.1. The structure of neurons 20 2.1.2. Types of neurons 21 2.2. Supporting cells 23 2.2.1. Supporting cells for central nervous system 23 2.2.2. Supporting cells for peripheral nervous system 25 2.2.3. Calcium wave 25 2.3. Nerve fibers 27 2.3.1. Myelinated fibers 27 2.3.2. Non-myelinated fibers 28 2.4. The nerve impulse 30 2.4.1. The resting potential 30 2.4.2. The action potential 31 2.4.3. Transmission of nerve impulses 33 2.5. Synapse 34 2.5.1. Synaptic structure 35 2.5.2. Synapse formation 35 2.5.3. Types of synapses 36 2.5.4. Synaptic plasticity 38 2.6. Network system 39 2.6.1. Complex Network Analysis of Brain Connectivity 39 2.6.2. Network properties 40 2.6.3. Network measures 42 2.6.4. Network models 43 Chapter 3. Models of the neuron 47 3.1. The Hodgkin-Huxley model 47 3.1.1. Basic components 47 3.1.2. Ionic current characterization 48 3.2. Neural networks 52 3.2.1. The connection probability 53 3.2.2. The coupled neural network 54 3.2.3. The synaptic strengths 57 3.2.4. Simulating the dynamics of coupled networks 59 3.2.5. Neuron synchronization 61 Chapter 4. Result and Discussion 63 4.1. Small world properties of single network 63 4.1.1. The characteristic path length (L) 63 4.1.2. The clustering coefficient (Ci) 65 4.2. Synchronous firing dynamics of a developing neural network. 68 4.2.1. The threshold value of network connections. 68 4.2.2. The phase diagram of a developing neural network. 70 4.2.3. The synchronization order parameter at various value of network connectivity. 74 4.3. Interactions of two neural layers. 76 4.3.1. Synchronization order parameter of BAS layer as a function of simulation time. 76 4.3.2. The time series of neuron firing and the average membrane potential of BAS layer induced by SFS layer with different A+. 79 4.3.3. The time series of neuron firing and the average membrane potential of BAS layer induced by SFS layer with different Nc. 80 4.3.4. The average synaptic weight of BAS layer. 81 4.3.5. The synchronization order parameter of a BAS layer varying with inter-layer connections. 83 4.3.6. Phase diagram with Nc2 variation. 84 4.3.7. Phase difference of two coupled SFS layers 86 4.4. Design of coupled neural networks. 88 4.4.1. Neuron positioning on the substrate. 88 4.4.2. The distribution of neurons degree. 90 4.4.3. Comparison between random, uniform, and log normal position for connected and disconnected inter-network connections with STDP learning. 91 4.4.4. Comparison between random and preferential inter-connections for three types neurons position (STDP). 94 4.4.5. Comparison between random, uniform, and log normal position for connected and disconnected inter-network connections with inverse STDP learning. 97 4.4.6. Comparison between random and preferential inter-connections for three types neurons position (inverse STDP). 100 4.4.7. The best design and the worst design in neuron positioning and inter-layer connections. 103 Chapter 5. Conclusion 105 References 106

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