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The hierarchical network model is part of the scale-free model family sharing their main property of having proportionally more hubs among the nodes than by random generation; however, it significantly differs from the other similar models (Barabási–Albert, Watts–Strogatz) in the distribution of the nodes' clustering coefficients: as other models would predict a constant clustering ...
Psi-theory suggests hierarchical networks of nodes as a universal mode of representation for declarative, procedural and tacit knowledge. These nodes may encode localist and distributed representations. The activity of the system is modeled using modulated and directional spreading of activation within these networks.
The model of hierarchical complexity (MHC), developed by Commons, is a way of measuring the complexity of a behavior. The MHC uses mathematical principles to quantify behavioral characteristics, assigning individuals to stages based on properly completed tasks.
Hierarchical network models are, by design, scale free and have high clustering of nodes. [33] The iterative construction leads to a hierarchical network. Starting from a fully connected cluster of five nodes, we create four identical replicas connecting the peripheral nodes of each cluster to the central node of the original cluster.
The model of hierarchical complexity (MHC) is a formal theory and a mathematical psychology framework for scoring how complex a behavior is. [4] Developed by Michael Lamport Commons and colleagues, [3] it quantifies the order of hierarchical complexity of a task based on mathematical principles of how the information is organized, [5] in terms of information science.
Many theoretical studies ask how the nervous system could implement Bayesian algorithms. Examples are the work of Pouget, Zemel, Deneve, Latham, Hinton and Dayan. George and Hawkins published a paper that establishes a model of cortical information processing called hierarchical temporal memory that is based on Bayesian network of Markov chains ...
Structural balance theory posits that some types of triads are forbidden and others are permitted on the basis of four rules. [4]Using the term “friend” to designate a positive sentiment and the term “enemy” to designate a negative sentiment, the classic balance model defines a sentiment network as balanced if it contains no violations of four assumptions:
When the HiTOP model is complete, it will form a detailed hierarchical classification system for mental illness starting from the most basic building blocks and proceeding to the highest level of generality: combining individual signs and symptoms into narrow components or traits, and then combining these symptom components and traits into (in ...