A nonclassical symbolic theory of working memory, mental computations, and mental set

TL;DR

This paper introduces a nonclassical symbolic framework called E-machine, explaining how the human brain can learn, simulate, recall, and adapt mental processes using dynamical memory states rather than classical symbolic manipulation.

Contribution

It proposes a novel nonclassical symbolic theory of brain function, formalized as E-machine, which is Turing universal and implementable in neural networks with cortical-like dynamics.

Findings

E-machine formalizes nonclassical symbolic processing in the brain.

The approach is Turing universal and neural network implementable.

It explains mental simulation, recall, and mental set adaptation.

Abstract

The paper tackles four basic questions associated with human brain as a learning system. How can the brain learn to (1) mentally simulate different external memory aids, (2) perform, in principle, any mental computations using imaginary memory aids, (3) recall the real sensory and motor events and synthesize a combinatorial number of imaginary events, (4) dynamically change its mental set to match a combinatorial number of contexts? We propose a uniform answer to (1)-(4) based on the general postulate that the human neocortex processes symbolic information in a "nonclassical" way. Instead of manipulating symbols in a read/write memory, as the classical symbolic systems do, it manipulates the states of dynamical memory representing different temporary attributes of immovable symbolic structures stored in a long-term memory. The approach is formalized as the concept of E-machine. Intuitively, an E-machine is a system that deals mainly with characteristic functions representing subsets of memory pointers rather than the pointers themselves. This nonclassical symbolic paradigm is Turing universal, and, unlike the classical one, is efficiently implementable in homogeneous neural networks with temporal modulation topologically resembling that of the neocortex.