Self-organization of position- and deformation-tolerant neural representations

The development of shift tolerance and deformation tolerance in neural representation is discussed with reference to a prototypical paradigm, which summarizes the essential problem of representation of a distribution of input patterns containing features that are distributed uniformly throughout an image space and that are subject to variation in form. A form of sparse, local representation is proposed in which the position of a feature is localized with precision proportional to the extent of the representation's tolerance to deformation of the feature, which in turn reflects the extent to which the form of that feature is subject to variation over the probability distribution of input patterns. A local self-organizing mechanism is described which inevitably generates representations of this form, regardless of the initial configuration of the synaptic strength parameters. The form of the representation established by this mechanism is unaffected by the inclusion of superfluous representation units: the ...