title: Stability criteria for the contextual emergence of macrostates in neural networks.
abstract: More than thirty years ago, Amari and colleagues proposed a statistical framework for identifying structurally stable macrostates of neural networks from observations of their microstates. We compare their stochastic stability criterion with a deterministic stability criterion based on the ergodic theory of dynamical systems, recently proposed for the scheme of contextual emergence and applied to particular inter-level relations in neuroscience. Stochastic and deterministic stability criteria for macrostates rely on macro-level contexts, which make them sensitive to differences between different macro-levels.
authors.affiliation: School of Psychology and Clinical Language Sciences, University of Reading, Reading, RG6 6AH, UK. p.r.beimgraben@reading.ac.uk
authors.forename: Peter
authors.initials: P
authors.lastname: beim Graben
authors.forename: Adam
authors.initials: A
authors.lastname: Barrett
authors.forename: Harald
authors.initials: H
authors.lastname: Atmanspacher
mesh_terms: D000465:Algorithms; D008390:Markov Chains; D016571:Neural Networks, Computer; D009474:Neurons; D013269:Stochastic Processes