title: Excitability changes that complement Hebbian learning.
abstract: Experiments have shown that the intrinsic excitability of neurons is not constant, but varies with physiological stimulation and during various learning paradigms. We study a model of Hebbian synaptic plasticity which is supplemented with intrinsic excitability changes. The excitability changes transcend time delays and provide a memory trace. Periods of selective enhanced excitability can thus assist in forming associations between temporally separated events, such as occur in trace conditioning. We demonstrate that simple bidirectional networks with excitability changes can learn trace conditioning paradigms.
authors.affiliation: Institute for Adaptive and Neural Computation, School of Informatics, 5 Forrest Hill, EH1 2QL, Edinburgh, UK.
authors.forename: Maia K
authors.initials: MK
authors.lastname: Janowitz
authors.forename: Mark C W
authors.initials: MC
authors.lastname: van Rossum
mesh_terms: D000818:Animals; D006801:Humans; D007858:Learning / Q000502:physiology*; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009473:Neuronal Plasticity / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission; D013997:Time Factors