title: Self-organisation of symmetry networks: transformation invariance from the spontaneous symmetry-breaking mechanism.
abstract: Symmetry networks use permutation symmetries among synaptic weights to achieve transformation-invariant response. This article proposes a generic mechanism by which such symmetries can develop during unsupervised adaptation: it is shown analytically that spontaneous symmetry breaking can result in the discovery of unknown invariances of the data's probability distribution. It is proposed that a role of sparse coding is to facilitate the discovery of statistical invariances by this mechanism. It is demonstrated that the statistical dependences that exist between simple-cell-like threshold feature detectors, when exposed to temporally uncorrelated natural image data, can drive the development of complex-cell-like invariances, via single-cell Hebbian adaptation. A single learning rule can generate both simple-cell-like and complex-cell-like receptive fields.
authors.affiliation: Defence Evaluation and Research Agency, Malvern WR14 3PS, U.K.
authors.forename: C J
authors.initials: CJ
authors.lastname: Webber
mesh_terms: D000200:Action Potentials / Q000502:physiology; D007858:Learning / Q000502:physiology; D008433:Mathematics; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D013569:Synapses / Q000502:physiology