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

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

Research output: Contribution to journalArticlepeer-review

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 Bienenstock-Cooper-Munro (BCM) learning rule, and single-cell independent component analysis. 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.

Original languageEnglish
Pages (from-to)1797-1813
Number of pages17
JournalNeural Computation
Volume10
Issue number7
DOIs
StatePublished - 1 Oct 1998
Externally publishedYes

Fingerprint

Dive into the research topics of 'Receptive Field Formation in Natural Scene Environments: Comparison of Single-Cell Learning Rules'. Together they form a unique fingerprint.

Cite this