Neuronal Micronets as Nodal Elements.

Abstract

We have been working on developing a computationally efficient way to emulate neurons and to emulate circuits and networks of same. We made considerable progress in compressing 'realistic' representations of neuronal computations into what we consider functionally equivalent input/output devices, which are now being incorporated into dynamic networks that learn associations and encode time. Our initial hypothesis about how to do this was rejected. Our new hypothesis offers great promise for scaling. This newer hypothesis resulted from examining simulations of 'realistic' neurons and thinking about the scaling problem. The latter was funded by the ONR.

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Document Details

Document Type
Technical Report
Publication Date
Sep 16, 1995
Accession Number
ADA310107

Entities

People

  • Thomas H. Brown

Organizations

  • Yale University

Tags

DTIC Thesaurus Topics

  • Application Software
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Brain
  • Cells
  • Classification
  • Computations
  • Confocal Laser Scanning Microscopy
  • Contract Administration
  • Experimental Data
  • Information Processing
  • Information Systems
  • Learning
  • Membranes
  • Nervous System
  • Neural Networks
  • Self Organizing Systems

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Educational Psychology
  • Neuroscience