Winner-Take-All Networks of O(N) Complexity

Abstract

Two general types of inhibition mediate activity in neural systems: subtractive inhibition, which sets a zero level for the computation, and multiplicative (nonlinear) inhibition, which regulates the gain of the computation. We report a physical realization of general nonlinear inhibition in its extreme form, known as winner-take-all We have designed and fabricated a series of compact, completely functional CMOS integrated circuits that realize the winner-take-all function, using the full analog nature of the medium. This circuit has been used successfully as a component in several VLSI sensory systems, that perform auditory localization (Lazzaro and Mead, in press) and visual stereopsis (Mahowald and Delbruck, 1988). Winner-take-all circuits with over 170 inputs function correctly in these sensory systems. We have also modified this global winner-take-all circuit, realizing a circuit that computes local nonlinear inhibition. The circuit allows multiple winners in the network, and is well suited for use in systems that represent a feature space topographically and that process several features in parallel. We have designed, fabricated, and tested a CMOS integrated circuit that computes locally the winner-take-all function of spatially ordered input.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1988
Accession Number
ADA451466

Entities

People

  • C. A. Mead
  • J. Lazzaro
  • M. A. Mahowald
  • S. Ryckebusch

Organizations

  • California Institute of Technology

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • Availability
  • Circuits
  • Classification
  • Complementary Metal-Oxide Semiconductors
  • Computations
  • Contracts
  • Electronics
  • Information Operations
  • Inhibition
  • Instructions
  • Integrated Circuits
  • Military Research
  • Monitoring
  • Networks
  • Security

Readers

  • Integrated Circuit Design and Technology.
  • Neural Network Machine Learning.
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • Space