scholarly journals Flow-based generative models for Markov chain Monte Carlo in lattice field theory

2019 ◽  
Vol 100 (3) ◽  
Author(s):  
M. S. Albergo ◽  
G. Kanwar ◽  
P. E. Shanahan
Keyword(s):  
Field Theory ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Generative Models ◽  
Lattice Field Theory ◽  
Lattice Field

scholarly journals Sampling Neuron Morphologies

2018 ◽  
Author(s):  
Roozbeh Farhoodi ◽  
Konrad Paul Kording
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Growth Process ◽  
Generative Models ◽  
Reversible Jump ◽  
Statistical Sampling ◽  
Imaging Data ◽  
Growth Processes ◽  
Sampling Process

AbstractThe intricate morphology of neurons has fascinated since the dawn of neuroscience, and yet, it is hard to synthesize them. Current algorithms typically define a growth process with parameters that allow matching aspects of the morphologies. However, such algorithmic growth processes are far simpler than the biological ones. What is needed is an algorithm that, given a database of morphologies, produces more of those. Here, we introduce a generator for neuron morphologies that is based on a statistical sampling process. Our Reversible Jump Markov chain Monte Carlo (RJMCMC) method starts with a trivial neuron and iteratively perturbs the morphology bringing the features close to those of the database. By quantifying the statistics of the generated neurons, we find that it outperforms growth-based models for many features. Good generative models for neuron morphologies promise to be important both for neural simulations and for morphology reconstructions from imaging data.

scholarly journals A first look at quasi-Monte Carlo for lattice field theory problems

2013 ◽  
Vol 454 ◽  
pp. 012043 ◽  
Author(s):  
K Jansen ◽  
H Leovey ◽  
A Nube ◽  
A Griewank ◽  
M Mueller-Preussker
Keyword(s):  
Field Theory ◽  
Monte Carlo ◽  
Lattice Field Theory ◽  
Quasi Monte Carlo ◽  
Lattice Field
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