scholarly journals Exploring the advantages of the maximum entropy model in calibrating cellular automata for urban growth simulation: a comparative study of four methods

2021 ◽  
pp. 1-25
Author(s):  
Bin Zhang ◽  
Haijun Wang
Keyword(s):  
Cellular Automata ◽  
Comparative Study ◽  
Maximum Entropy ◽  
Urban Growth ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Growth Simulation

Simulation of urban expansion based on cellular automata and maximum entropy model

2020 ◽  
Vol 63 (5) ◽  
pp. 701-712 ◽  
Author(s):  
Yihan Zhang ◽  
Xiaoping Liu ◽  
Guangliang Chen ◽  
Guohua Hu
Keyword(s):  
Cellular Automata ◽  
Maximum Entropy ◽  
Urban Expansion ◽  
Maximum Entropy Model ◽  
Entropy Model

scholarly journals Pairwise maximum entropy model explains the role of white matter structure in shaping emergent co-activation states

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Arian Ashourvan ◽  
Preya Shah ◽  
Adam Pines ◽  
Shi Gu ◽  
Christopher W. Lynn ◽  
...  
Keyword(s):  
White Matter ◽  
Maximum Entropy ◽  
Large Scale ◽  
Structural Connectivity ◽  
Quantitative Relationship ◽  
Brain Regions ◽  
Maximum Entropy Model ◽  
Entropy Model ◽  
Activation Patterns ◽  
Co Activation

AbstractA major challenge in neuroscience is determining a quantitative relationship between the brain’s white matter structural connectivity and emergent activity. We seek to uncover the intrinsic relationship among brain regions fundamental to their functional activity by constructing a pairwise maximum entropy model (MEM) of the inter-ictal activation patterns of five patients with medically refractory epilepsy over an average of ~14 hours of band-passed intracranial EEG (iEEG) recordings per patient. We find that the pairwise MEM accurately predicts iEEG electrodes’ activation patterns’ probability and their pairwise correlations. We demonstrate that the estimated pairwise MEM’s interaction weights predict structural connectivity and its strength over several frequencies significantly beyond what is expected based solely on sampled regions’ distance in most patients. Together, the pairwise MEM offers a framework for explaining iEEG functional connectivity and provides insight into how the brain’s structural connectome gives rise to large-scale activation patterns by promoting co-activation between connected structures.

Video segmentation using Maximum Entropy Model

2005 ◽  
Vol 6 (S1) ◽  
pp. 47-52
Author(s):  
Li-juan Qin ◽  
Yue-ting Zhuang ◽  
Yun-he Pan ◽  
Fei Wu
Keyword(s):  
Maximum Entropy ◽  
Video Segmentation ◽  
Maximum Entropy Model ◽  
Entropy Model
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