Stability criteria for the contextual emergence of macrostates in neural networksbeim 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 Full text not archived in this repository. It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1080/09548980903161241 Abstract/SummaryMore 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.
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