title: Synergies between intrinsic and synaptic plasticity mechanisms.
abstract: We propose a model of intrinsic plasticity for a continuous activation model neuron based on information theory. We then show how intrinsic and synaptic plasticity mechanisms interact and allow the neuron to discover heavy-tailed directions in the input. We also demonstrate that intrinsic plasticity may be an alternative explanation for the sliding threshold postulated in the BCM theory of synaptic plasticity. We present a theoretical analysis of the interaction of intrinsic plasticity with different Hebbian learning rules for the case of clustered inputs. Finally, we perform experiments on the "bars" problem, a popular nonlinear independent component analysis problem.
authors.affiliation: Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe University, 60438 Frankfurt am Main, Germany. triesch@fias.uni-frankfurt.de
authors.forename: Jochen
authors.initials: J
authors.lastname: Triesch
mesh_terms: D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology*