As One and Many: Relating Individual and Emergent Group-Level Generative Models in Active Inference [PDF]
EntropyActive inference under the Free Energy Principle has been proposed as an across-scales compatible framework for understanding and modelling behaviour and self-maintenance.
Peter Thestrup Waade +6 more
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Explainable artificial intelligence approaches for predicting depression by combining feature selection methods and machine learning classifiers [PDF]
Digital HealthObjective Depression represents a significant global health challenge, further complicated by the multifaceted and complex nature of its diagnosis and treatment.
Min Gyeong Kim +5 more
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Minimization of prediction errors during cerebral embryogenesis and the emergence of agency [PDF]
Frontiers in Systems NeuroscienceA theory of self-organization in the central nervous system is described, proposing that additive and dissipative synaptodendritic summation leads to synchronous oscillation as the equilibrium state, thereby underpinning a primary mechanism of prediction
James Joseph Wright, Paul David Bourke
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Thoughtseeds: A Hierarchical and Agentic Framework for Investigating Thought Dynamics in Meditative States [PDF]
EntropyThe Thoughtseeds Framework introduces a novel computational approach to modeling thought dynamics in meditative states, conceptualizing thoughtseeds as dynamic attentional agents that integrate information.
Prakash Chandra Kavi +3 more
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Emergent classicality in general multipartite states and channels [PDF]
Quantum, 2021 In a quantum measurement process, classical information about the measured system spreads throughout the environment. Meanwhile, quantum information about the system becomes inaccessible to local observers.
Xiao-Liang Qi, Daniel Ranard
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The Emperor's New Markov Blankets [PDF]
Behavioral and Brain Sciences, 2021 AbstractThe free energy principle, an influential framework in computational neuroscience and theoretical neurobiology, starts from the assumption that living systems ensure adaptive exchanges with their environment by minimizing the objective function of variational free energy.
Jelle Bruineberg +3 more
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Analyzing Markov Boundary Discovery Algorithms in Ideal Conditions Using the d-Separation Criterion
Algorithms, 2022 This article proposes the usage of the d-separation criterion in Markov Boundary Discovery algorithms, instead of or alongside the statistical tests of conditional independence these algorithms usually rely on.
Camil Băncioiu, Remus Brad
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Parcels and particles: Markov blankets in the brain [PDF]
Network Neuroscience, 2021 At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions—and analyses—of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed
Friston, KJ +6 more
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Reverse engineering of modified genes by Bayesian network analysis defines molecular determinants critical to the development of glioblastoma. [PDF]
PLoS ONE, 2013 In this study we have identified key genes that are critical in development of astrocytic tumors. Meta-analysis of microarray studies which compared normal tissue to astrocytoma revealed a set of 646 differentially expressed genes in the majority of ...
Brian W Kunkle +2 more
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Causal Learner: A Toolbox for Causal Structure and Markov Blanket Learning [PDF]
Pattern Recognition Letters, 2021 Causal Learner is a toolbox for learning causal structure and Markov blanket (MB) from data. It integrates functions for generating simulated Bayesian network data, a set of state-of-the-art global causal structure learning algorithms, a set of state-of ...
Zhaolong Ling +4 more
semanticscholar +1 more source