title: Informational properties of neural nets performing algorithmic and logical tasks.
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 n 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.
authors.affiliation: Department of Computer Science and Engineering, University of California, San Diego, La Jolla 92093-0114, USA.
authors.forename: B M
authors.initials: BM
authors.lastname: Ritz
authors.forename: G L
authors.initials: GL
authors.lastname: Hofacker
mesh_terms: D000465:Algorithms; D007257:Information Theory; D008128:Logic; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D013816:Thermodynamics