ENTROPY MINIMIZATION: A DESIGN PRINCIPLE FOR SENSORY PERCEPTION?

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Abstract
We speculate on how computational strategies suggested by Information theory could be useful for knowledge acquisition in sensory systems. The focus of our exploration is the idea that perception is a data reduction problem and as such sensory transformations should be predictable from the data reduction problem and as such sensory transformations should be predictable terms of variational principles involving the minimization of two types of entropy. The analysis suggests a scheme in which the two can be combined to produce a predictive design principle for sensory pathways.

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