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Markov Brains: A Technical Introduction [article]

Arend Hintze, Jeffrey A. Edlund, Randal S. Olson, David B. Knoester, Jory Schossau, Larissa Albantakis, Ali Tehrani-Saleh, Peter Kvam, Leigh Sheneman, Heather Goldsby, Clifford Bohm, Christoph Adami
2017 arXiv   pre-print
Markov Brains are a class of evolvable artificial neural networks (ANN).  ...  Here we describe in detail how a Markov Brain works, what techniques can be used to study them, and how they can be evolved.  ...  As a consequence, each row of the matrix P needs to sum to 1.0: In this report we use a capital B for brains that are specifically artificial brains of the Markov type.  ... 
arXiv:1709.05601v1 fatcat:egzd3g76sfbazifnzvm5yxn3vy

Markov Blankets in the Brain [article]

Ines Hipolito, Maxwell Ramstead, Laura Convertino, Anjali Bhat, Karl Friston, Thomas Parr
2020 arXiv   pre-print
Recent characterisations of self-organising systems depend upon the presence of a Markov blanket: a statistical boundary that mediates the interactions between what is inside of and outside of a system  ...  This depends upon the dynamic coupling between functional units, whose form recapitulates that of a Markov blanket at each level.  ...  Introduction Scientific investigation in neurobiology often begins -perhaps only implicitly -by partitioning the brain into functional units.  ... 
arXiv:2006.02741v1 fatcat:w7vv2ndzajea7crwdivndamy3q

The Emperor's New Markov Blankets

Jelle Bruineberg, Krzysztof Dolega, Joe Dewhurst, Manuel Baltieri
2021 Behavioral and Brain Sciences  
We then identify a persistent confusion in the literature between the formal use of Markov blankets as an epistemic tool for Bayesian inference, and their novel metaphysical use in the free energy framework  ...  We suggest that this literature would do well in differentiating between two different research programs: 'inference with a model' and 'inference within a model'.  ...  are listed in the introduction).  ... 
doi:10.1017/s0140525x21002351 pmid:34674782 fatcat:splnqu7npnhvhhu73ra3szyl3e

Parcels and particles: Markov blankets in the brain [article]

Karl J. Friston, Erik D. Fagerholm, Tahereh S. Zarghami, Thomas Parr, Inês Hipólito, Loïc Magrou, Adeel Razi
2020 arXiv   pre-print
It is predicated on the notion of Markov blankets that play a fundamental role in the self-organisation of far from equilibrium systems.  ...  As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.  ...  Because this paper is a technical (foundational) description of the procedures entailed by the existence of Markov blanketsof Markov blanketswe have focused on the simplest implementation.  ... 
arXiv:2007.09704v1 fatcat:akmutwfnercjfcdwrt3i5sd3iu

Markov random field segmentation of brain MR images

K. Held, E.R. Kops, B.J. Krause, W.M. Wells, R. Kikinis, H.-W. Muller-Gartner
1997 IEEE Transactions on Medical Imaging  
We describe a fully-automatic 3D-segmentation technique for brain MR images.  ...  A simulated annealing and an iterated conditional modes implementation are presented. Keywords: Magnetic Resonance Imaging, Segmentation, Markov Random Fields  ...  Tellmann for their advice and technical assistance. They would also like to thank J. Shah for assistance in preparing the manuscript.  ... 
doi:10.1109/42.650883 pmid:9533587 fatcat:ullbj73mbbabhc7y24snla7n34

Parcels and particles: Markov blankets in the brain

Karl J. Friston, Erik D. Fagerholm, Tahereh S. Zarghami, Thomas Parr, Inês Hipólito, Loïc Magrou, Adeel Razi
2020 Network Neuroscience  
It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems.  ...  As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.  ...  Because this paper is a technical (foundational) description of the procedures entailed by the existence of Markov blankets, we have focused on the simplest implementation.  ... 
doi:10.1162/netn_a_00175 pmid:33688613 pmcid:PMC7935044 fatcat:gshs2lvrezeaffys6nl3kpj2bm

Markov chain Monte Carlo methods for hierarchical clustering of dynamic causal models

Yu Yao, Klaas E Stephan
2021 Human Brain Mapping  
brain connectivity.  ...  In this article, we address technical difficulties that arise when applying Markov chain Monte Carlo (MCMC) to hierarchical models designed to perform clustering in the space of latent parameters of subject-wise  ...  This technical note addresses the specific problem of applying Markov chain Monte Carlo (MCMC) to hierarchical clustering in the context of generative embedding (GE) .  ... 
doi:10.1002/hbm.25431 pmid:33826194 pmcid:PMC8193526 fatcat:22vsdofe7fg2zl5nebdqdmmwua

Verifying Reachability Properties in Markov Chains via Incremental Induction [article]

Elizabeth Polgreen, Martin Brain, Martin Fraenzle, Alessandro Abate
2019 arXiv   pre-print
Markov chains.  ...  There is a scalability gap between probabilistic and non-probabilistic verification.  ...  The technical contributions in this paper are: -In Section 3 we present an algorithm for verifying reachability properties in Markov chains, which uses incrementally constructed inductive invariants to  ... 
arXiv:1909.08017v2 fatcat:uqa3ll4glnhndemhfine2hrl2q

Semi-Markov and hidden semi-Markov models of energy systems

Yuriy E. Obzherin, N. Voropai, S. Senderov, A. Michalevich, H. Guliev
2018 E3S Web of Conferences  
The possibilities of application of semi-Markov processes with common phase space of states, hidden Markov and semi-Markov models for energy system modeling are considered in the paper.  ...  , hidden semi-Markov models, their application for energy system modelling Semi-Markov processes are widely used to construct models and analyse systems of different purposes: technical, production, energy  ...  Introduction According to the road map of National Technology Initiative "EnergyNet" between the main purposes of this project are: 1."  ... 
doi:10.1051/e3sconf/20185802023 fatcat:5n6qkckmorehbmkkxu7ijuotz4

Client-Centred Music Imagery Classification Based on Hidden Markov Models of Baseline Prefrontal Hemodynamic Response [chapter]

Tiago H., Kelly M., Tom Chau
2013 Brain-Computer Interface Systems - Recent Progress and Future Prospects  
Hidden Markov models A hidden Markov model is a statistical model which examines a Markov process wherein the observable outputs are dependent upon the unobservable states.  ...  Client-Centred Music Imagery Classification Based on Hidden Markov Models ... http://dx.doi.org/10.5772/55804 Brain-Computer Interface Systems -Recent Progress and Future Prospects  ... 
doi:10.5772/55804 fatcat:gle6m7zhanh6jf5vu6lonn4ity

Region Detection in Markov Random Fields: Gaussian Case [article]

Ilya Soloveychik, Vahid Tarokh
2018 arXiv   pre-print
We consider the problem of model selection in Gaussian Markov fields in the sample deficient scenario.  ...  bounds and show that a bounded number of samples can be sufficient to consistently recover these regions.  ...  INTRODUCTION A.  ... 
arXiv:1802.03848v8 fatcat:v6i4yspchjbz7km5vioaj2k4wu

Context-Aware Hidden Markov Models of Jazz Music with Variable Markov Oracle

Cheng-I Wang, Shlomo Dubnov
2017 Zenodo  
The proposed latent variable model is a multi-level Hidden Markov Model (HMM) extracted from the VMO, and is called the VMO-HMM.  ...  In this paper, a latent variable model based on the Variable Markov Oracle (VMO) is proposed to capture long-term temporal relationships between sequential observations.  ...  The technical details of constructing a VMO could be found in (Wang, Hsu, and Dubnov 2016) and are not repeated here.  ... 
doi:10.5281/zenodo.4285246 fatcat:cgpfd52jpzgebnbx2v2rirylzm

Unsupervised learning and mapping of active brain functional MRI signals based on hidden semi-Markov event sequence models

S. Faisan, L. Thoraval, J.-P. Armspach, M.-N. Metz-Lutz, F. Heitz
2005 IEEE Transactions on Medical Imaging  
In this paper, a novel functional magnetic resonance imaging (fMRI) brain mapping method is presented within the statistical modeling framework of hidden semi-Markov event sequence models (HSMESMs).  ...  Index Terms-Brain mapping, functional MRI, hidden Markov models, signal processing, wavelet transform.  ...  INTRODUCTION C OMMONLY used techniques in functional MRI (fMRI) brain mapping can be divided into two classes: data-driven and model-driven.  ... 
doi:10.1109/tmi.2004.841225 pmid:15707252 fatcat:fhlkgeuciradlbrx2wyt7mg3ii

Continuous-Time Markov Chains [chapter]

2016 Introduction to Stochastic Processes With R  
Introduction We now turn to continuous-time Markov chains (CTMC's), which are a natural sequel to the study of discrete-time Markov chains (DTMC's), the Poisson process and the exponential distribution  ...  (Pooh Bear and the Three Honey Trees) A bear of little brain named Pooh is fond of honey. Bees producing honey are located in three trees: tree A, tree B and tree C.  ... 
doi:10.1002/9781118740712.ch7 fatcat:55dijthetrh3dpjo7scxlvvgmy

Markovian Segmentation of Brain Tumor MRI Images

Meryem Ameur, Cherki Daoui, Najlae Idrissi
2017 International Journal of Informatics and Communication Technology (IJ-ICT)  
These algorithms are used to segment brain tumor Magnetic Resonance Imaging (MRI) images, under Hidden Markov Chain with Indepedant Noise (HMC-IN).  ...  <p>Image segmentation is a fundamental operation in image processing, which consists to di-vide an image in the homogeneous region for helping a human to analyse image, to diagnose a disease and take the  ...  We are used MPM Algorithm [5] to estimate a final configuration of X. Also, we extract a brain tumor using thresholding technic [11] .  ... 
doi:10.11591/ijict.v6i3.pp155-165 fatcat:aadysvicbjebfhl6hfikp6qceq
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