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  • V. Eliashberg
  • 1989
Most of the currently popular neural models of associative memory ignore nontrivial temporal processes in neural elements. Such nontemporal models describe interesting effects of spatial cooperation but do not address the problem of temporal coordination and temporal context. Effects such as temporal association, short-term memory, and mental set can be(More)
The author describes the dynamics of a neuron layer with reciprocal inhibition. In the presence of noise the layer works as a selector, performing random choice of a single neuron from the set of neurons with the maximum level of excitation. He discusses the following topics: two-basic-layer architectures with reciprocal inhibition; the Laplace transforms(More)
In the theory of context-sensitive associative memory (CSAM) described in [1, 2, 3, 4] a broad range of psychological phenomena of short-term memory (STM) and temporal context (mental set) can be naturally understood as implications of the states of " residual excitation " in neural elements. Such hypothetical states of analog dynamic memory were referred(More)
A universal learning neurocomputer is a brain– inspired information processing system that can be taught to perform, in principle, an arbitrary algorithm (universal in Turing's sense). Conventional computers and universal Turing machines are examples of universal programmable systems. However, the process of programming these systems does not match our(More)
Turing's "Machines". These machines are humans who calculate. 1.0 Introduction This paper goes back to Turing (1936) and treats his machine as a cognitive model (W,D,B), where W is an "external world" represented by memory device (the tape divided into squares), and (D,B) is a simple robot that consists of the sensory-motor devices, D, and the brain, B. The(More)
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)(More)
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(More)