Brain Behavior Evolution during Learning: Emergence of Hierarchical Temporal Memory

Abstract

In this report, we summarize two attempts to ascertain whether structure arises in Hopfield brain models subject to Hebbian learning. The first attempt uses graph measures to derive numerical scores for networks, and then apply data mining methods to separate the networks in parameter space. The second attempt uses one- and two-event chains to relate synapse strength to connection information. Learning methods are applied to brains of different types to see if changes can be detected. No Hebbian learning method caused tree-like structure to develop.

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Document Details

Document Type
Technical Report
Publication Date
Aug 30, 2013
Accession Number
ADA608125

Entities

People

  • Donald A. Drew

Organizations

  • Rensselaer Polytechnic Institute

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Artificial Intelligence Software
  • Bayesian Networks
  • Brain
  • Cerebral Cortex
  • Computational Science
  • Computers
  • Data Mining
  • Dimensionality Reduction
  • Gaussian Distributions
  • Information Processing
  • Information Science
  • Machine Learning
  • Medical Personnel
  • Network Science
  • Neural Networks
  • Supervised Machine Learning
  • Three Dimensional

Fields of Study

  • Biology

Readers

  • Neural Network Machine Learning.
  • Neuroscience
  • Systems Analysis and Design

Technology Areas

  • AI & ML
  • Space