LENGTHS OF CYCLE TIMES IN RANDOM NEURAL NETWORKS,

Abstract

The report contains a feasibility study of timing circuits for a model of the human brain. It is shown that certain random neural networks have cycle times which increase exponentially with the size of the network, and that these networks may be used as timing devices even for a period of time equal to a human lifetime. The methods used in this report are those of probability, combinatorial analysis, and number theory. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 15, 1967
Accession Number
AD0659508

Entities

People

  • Neil James Alexander Sloane

Organizations

  • Cornell University

Tags

DTIC Thesaurus Topics

  • Circuits
  • Combinatorial Analysis
  • Feasibility Studies
  • Mathematics
  • Networks
  • Neural Networks
  • Number Theory
  • Numbers
  • Probability
  • Timing Circuits
  • Timing Devices

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Mathematical Modeling and Probability Theory.
  • Neuroscience

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks