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

TL;DR

This paper introduces the concept of E-machines, hypothesizing how brain-like systems can dynamically reconfigure to process symbolic information through associative learning, addressing fundamental questions about neural universality and adaptability.

Contribution

It proposes a theoretical framework for brain-like systems that can learn and reconfigure dynamically to process symbolic information, bridging neural and symbolic computation.

Findings

Introduces the E-machine concept for dynamic reconfiguration

Explores how neural networks can function as universal symbolic machines

Addresses the formation of complex software through associative learning

Abstract

The 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?