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Stability criteria for the contextual emergence of macrostates in neural networks

beim Graben, P., Barrett, A. and Atmanspacher, H. (2009) Stability criteria for the contextual emergence of macrostates in neural networks. Network-Computation in Neural Systems, 20 (3). pp. 178-196. ISSN 0954-898X

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To link to this article DOI: 10.1080/09548980903161241

Abstract/Summary

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.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Life Sciences > School of Psychology and Clinical Language Sciences
ID Code:14160
Uncontrolled Keywords:Network models, STATISTICAL NEURODYNAMICS, DYNAMICAL-SYSTEMS, KMS STATES

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