title: Recall of old and recent information.
abstract: A neural network model for 'forgetting upon learning' is developed in such a way that old information can also be evoked. A new synaptic clipping scheme coupled with selective reinforcement of information is introduced that mimics the learning and memory functions of the 'limbic system' in the brain. The model thus enables both long-term (old) and short-term (recent) memories to exist concurrently, without one affecting the other, as one expects in a realistic situation.
authors.affiliation: Cavendish Laboratory, University of Cambridge, UK.
authors.forename: V
authors.initials: V
authors.lastname: Srivastava
authors.forename: M
authors.initials: M
authors.lastname: Vipin
authors.forename: E
authors.initials: E
authors.lastname: Granato
mesh_terms: D006801:Humans; D008032:Limbic System / Q000502:physiology; D008570:Memory, Short-Term / Q000502:physiology*; D011939:Mental Recall / Q000502:physiology*; D016571:Neural Networks, Computer