title: Linear analysis of auto-organization in Hebbian neural networks.
abstract: The self-organization of neurotopies where neural connections follow Hebbian dynamics is framed in terms of linear operator theory. A general and exact equation describing the time evolution of the overall synaptic strength connecting two neural laminae is derived. This linear matricial equation, which is similar to the equations used to describe oscillating systems in physics, is modified by the introduction of non-linear terms, in order to capture self-organizing (or auto-organizing) processes. The behavior of a simple and small system, that contains a non-linearity that mimics a metabolic constraint, is analyzed by computer simulations. The emergence of a simple "order" (or degree of organization) in this low-dimensionality model system is discussed.
authors.affiliation: Departamento de Biología, Universidad de Chile, Santiago. letelier@abello.seci.uchile.cl
authors.forename: J
authors.initials: J
authors.lastname: Carlos Letelier
authors.forename: J
authors.initials: J
authors.lastname: Mpodozis
mesh_terms: D003198:Computer Simulation; D016014:Linear Models; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*