Receptive Field Formation in Natural Scene Environments: Comparison of Single Cell Learning Rules

Abstract

We study several statistically and biologically motivated learning rules using the same visual environment, one made up of natural scenes, and the same single cell neuronal architecture. This allows us to concentrate on the feature extraction and neuronal coding properties of these rules. Included in these rules are kurtosis and skewness maximization, the quadratic form of the BCM learning rule, and single cell ICA. Using a structure removal method, we demonstrate that receptive fields developed using these rules depend on a small portion of the distribution. We find that the quadratic form of the BCM rule behaves in a manner similar to a kurtosis maximization rule when the distribution contains kurtotic directions, although the BCM modification equations are computationally simpler.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Dec 10, 1997
Accession Number
ADA333495

Entities

People

  • Brian S. Blais
  • H. Shouval
  • Leon Cooper
  • N. Intrator

Organizations

  • Brown University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Coding
  • Computations
  • Computer Programming
  • Data Analysis
  • Feature Extraction
  • Gaussian Distributions
  • Information Processing
  • Information Science
  • Information Systems
  • Information Theory
  • Mathematical Analysis
  • Neural Networks
  • Orientation (Direction)
  • Signal Processing
  • Skewness
  • Statistics

Readers

  • Computer Vision.
  • Neuroscience
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Machine Translation