Informational properties of neural nets performing algorithmic and logical tasks |
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Authors: | Barbara M Ritz G Ludwig Hofacker |
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Institution: | 1. Department of Computer Science and Engineering, University of California, San Diego, 92093-0114, La Jolla, CA, USA 2. Institut für Physikalische und Theoretische Chemie, Technische Universit?t München, D-85747, Garching bei München, Germany
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Abstract: | It is argued that the genetic information necessary to encode an algorithmic neural processor tutoring an otherwise randomly
connected biological neural net is represented by the entropy of the analogous minimal Turing machine. Such a near-minimal
machine is constructed performing the whole range of bivalent propositional logic in variables. Neural nets computing the same
task are presented; their informational entropy can be gauged with reference to the analogous Turing machine. It is also shown
that nets with one hidden layer can be trained to perform algorithms solving propositional logic by error back-propagation.
Received: 30 June 1995 / Accepted in revised form: 9 January 1996 |
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