title: A simple Hebbian/anti-Hebbian network learns the sparse, independent components of natural images.
abstract: Slightly modified versions of an early Hebbian/anti-Hebbian neural network are shown to be capable of extracting the sparse, independent linear components of a prefiltered natural image set. An explanation for this capability in terms of a coupling between two hypothetical networks is presented. The simple networks presented here provide alternative, biologically plausible mechanisms for sparse, factorial coding in early primate vision.
authors.affiliation: School of Psychology, University of Western Australia, Nedlands WA 6009, Australia. michaelf@psy.uwa.edu.au
authors.forename: Michael S
authors.initials: MS
authors.lastname: Falconbridge
authors.forename: Robert L
authors.initials: RL
authors.lastname: Stamps
authors.forename: David R
authors.initials: DR
authors.lastname: Badcock
mesh_terms: D000465:Algorithms; D000818:Animals; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D014793:Visual Cortex / Q000502:physiology*