A Study of Neuronal Properties, Synaptic Plasticity and Network Interactions Using a Computer Reconstituted Neuronal Network Derived from Fundamental Biophysical Principles

1992 ◽  
Author(s):  
David C. Tam
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Network

The collective bursting dynamics in a modular neuronal network with synaptic plasticity

2017 ◽  
Vol 89 (4) ◽  
pp. 2593-2602 ◽  
Author(s):  
Xiao Li Yang ◽  
Jia Yi Wang ◽  
Zhong Kui Sun
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Network ◽  
Modular Neuronal Network

EXCITEMENT AND SYNCHRONIZATION OF SMALL-WORLD NEURONAL NETWORKS WITH SHORT-TERM SYNAPTIC PLASTICITY

2011 ◽  
Vol 21 (05) ◽  
pp. 415-425 ◽  
Author(s):  
FANG HAN ◽  
MARIAN WIERCIGROCH ◽  
JIAN-AN FANG ◽  
ZHIJIE WANG
Keyword(s):  
Synaptic Plasticity ◽  
Neuronal Network ◽  
Coupling Strength ◽  
Neuronal Networks ◽  
Learning Rule ◽  
Small World ◽  
Learning Rate ◽  
Short Term ◽  
Synaptic Learning

Excitement and synchronization of electrically and chemically coupled Newman-Watts (NW) small-world neuronal networks with a short-term synaptic plasticity described by a modified Oja learning rule are investigated. For each type of neuronal network, the variation properties of synaptic weights are examined first. Then the effects of the learning rate, the coupling strength and the shortcut-adding probability on excitement and synchronization of the neuronal network are studied. It is shown that the synaptic learning suppresses the over-excitement, helps synchronization for the electrically coupled network but impairs synchronization for the chemically coupled one. Both the introduction of shortcuts and the increase of the coupling strength improve synchronization and they are helpful in increasing the excitement for the chemically coupled network, but have little effect on the excitement of the electrically coupled one.

scholarly journals Inferring neuronal network functional connectivity with directed information

2017 ◽  
Vol 118 (2) ◽  
pp. 1055-1069 ◽  
Author(s):  
Zhiting Cai ◽  
Curtis L. Neveu ◽  
Douglas A. Baxter ◽  
John H. Byrne ◽  
Behnaam Aazhang
Keyword(s):  
Information Theory ◽  
Synaptic Plasticity ◽  
Functional Connectivity ◽  
Neuronal Network ◽  
Data Driven ◽  
Inference Method ◽  
Motor Patterns ◽  
Functional Connectome ◽  
Directed Information ◽  
Statistical Approaches

This study brings together the techniques of voltage-sensitive dye recording and information theory to infer the functional connectome of the feeding central pattern generating network of Aplysia. In contrast to current statistical approaches, the inference method developed in this study is data driven and validated by conductance-based model circuits, can distinguish excitatory and inhibitory connections, is robust against synaptic plasticity, and is capable of detecting network structures that mediate motor patterns.

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