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The neural network as a renormalizer of information
Author(s):
Willard
L.
Miranker
Journal:
Quart. Appl. Math.
66
(2008),
379-394.
MSC (2000):
Primary 91E30, 46N60
Posted:
February 8, 2008
MathSciNet review:
2416779
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Abstract:
We characterize the behavior of information in neural processing as the neuronal circuitry itself agglomerates into assemblies of increasing size and complexity. The basic synaptic stage of this processing is interpreted as the observer feature of a measurement process, a quality that extends up the assembly hierarchy. Renormalization techniques are employed, and they supply features of emergence to the information. Renormalization also supplies each observer feature with a measurable physical quantity called a token, the latter supplying quantitative aspects to the entire development. This development is used to frame an analytic theory of phenomenal consciousness, featuring emergent aspects. The tokens furnish the means for the various predictions and explanations of that theory to be subjected to measurement and experimental verification.
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Additional Information:
Willard
L.
Miranker
Affiliation:
Department of Computer Science, Yale University, New Haven, CT
PII:
S0033-569X-08-01073-4
Keywords:
Consciousness,
dualist construct,
emergence,
Hebbian dynamics,
measurement,
neural networks,
phase change,
renormalization
Received by editor(s):
January 4, 2007
Posted:
February 8, 2008
Copyright of article:
Copyright
2008,
Brown University
The copyright for this article reverts to public domain after 28 years from publication.
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