Concept of E-machine: How does a "dynamical" brain learn to process "symbolic" information? Part I

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dc.creatorEliashberg, Victor
dc.date2004-03-19
dc.date2004-03-20
dc.date.accessioned2026-07-07T03:21:02Z
dc.date.available2026-07-07T03:21:02Z
dc.descriptionThe human brain has many remarkable information processing characteristics that deeply puzzle scientists and engineers. Among the most important and the most intriguing of these characteristics are the brain's broad universality as a learning system and its mysterious ability to dynamically change (reconfigure) its behavior depending on a combinatorial number of different contexts. This paper discusses a class of hypothetically brain-like dynamically reconfigurable associative learning systems that shed light on the possible nature of these brain's properties. The systems are arranged on the general principle referred to as the concept of E-machine. The paper addresses the following questions: 1. How can "dynamical" neural networks function as universal programmable "symbolic" machines? 2. What kind of a universal programmable symbolic machine can form arbitrarily complex software in the process of programming similar to the process of biological associative learning? 3. How can a universal learning machine dynamically reconfigure its software depending on a combinatorial number of possible contexts?
dc.description40 pages, 15 figures. The author is a consulting professor at the Stanford University, Department of Electrical Engineering and the president of Avel Electronics (consulting company)
dc.identifierhttps://arxiv.org/abs/cs/0403031
dc.identifierhttp://arxiv.org/abs/cs/0403031
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/32047
dc.subjectArtificial Intelligence
dc.subjectMachine Learning
dc.subjectI.2.0
dc.titleConcept of E-machine: How does a "dynamical" brain learn to process "symbolic" information? Part I
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