|InterJournal Complex Systems, 428
|Manuscript Number: |
Submission Date: 803
|Information resonance and pattern recognition in classical and quantum systems: toward a 'language model' of hierarchical neural structure and process|
Subject(s): CX.07, CX.08, CX.14
Recent applications of the Shannon-McMillan Theorem to arrays of nonlinear components undergoing what is effectively an 'information resonance' (R Wallace, 2000, IJBC, Vol. 10 No. 2, 493-502) may be extended to include many neural models, both classical and quantum. Some consideration reduces the threefold interacting complex of sensory activity, ongoing activity, and nonlinear oscillator to a single object, a parametized, ergodic information source. Invocation of the 'large deviations' program of applied probability that unifies treatment of dynamical fluctuations, statistical mechanics and information theory allows a 'natural' transfer of thermodynamic and renormalization arguments to information theory, permitting a markedly simplified analysis of neural dynamics. This suggests an inherent language-based foundation, in a large sense, to neural structure and process, and implies that approaches without intimate relation to languagemay be seriously incomplete.
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