title: Combining Hebbian and reinforcement learning in a minibrain model.
abstract: A toy model of a neural network in which both Hebbian learning and reinforcement learning occur is studied. The problem of 'path interference', which makes that the neural net quickly forgets previously learned input-output relations is tackled by adding a Hebbian term (proportional to the learning rate nu) to the reinforcement term (proportional to delta) in the learning rule. It is shown that the number of learning steps is reduced considerably if 1/4<nu/delta<1/2, i.e. if the Hebbian term is neither too small nor too large compared to the reinforcement term.
authors.affiliation: Institute for Theoretical Physics, University of Amsterdam, Valckenierstraat 65, 1018 XE Amsterdam, The Netherlands.
authors.forename: R J C
authors.initials: RJ
authors.lastname: Bosman
authors.forename: W A
authors.initials: WA
authors.lastname: van Leeuwen
authors.forename: B
authors.initials: B
authors.lastname: Wemmenhove
mesh_terms: D000818:Animals; D003198:Computer Simulation; D003213:Conditioning, Psychological; D030141:Feedback, Psychological; D006801:Humans; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D016571:Neural Networks, Computer; D012054:Reinforcement, Psychology; D013269:Stochastic Processes