title: A multi-layer neural-mass model for learning sequences using theta/gamma oscillations.
abstract: A neural mass model for the memorization of sequences is presented. It exploits three layers of cortical columns that generate a theta/gamma rhythm. The first layer implements an auto-associative memory working in the theta range; the second segments objects in the gamma range; finally, the feedback interactions between the third and the second layers realize a hetero-associative memory for learning a sequence. After training with Hebbian and anti-Hebbian rules, the network recovers sequences and accounts for the phase-precession phenomenon.
authors.affiliation: Department of Electronics, Computer Sciences and Systems, University of Bologna, Via Venezia, 52, Cesena (FC), 47521, Italy. filippo.cona2@unibo.it
authors.forename: Filippo
authors.initials: F
authors.lastname: Cona
authors.forename: Mauro
authors.initials: M
authors.lastname: Ursino
mesh_terms: D001245:Association Learning / Q000502:physiology*; D002540:Cerebral Cortex / Q000166:cytology* / Q000502:physiology; D030141:Feedback, Psychological; D006801:Humans; D011939:Mental Recall / Q000502:physiology; D008959:Models, Neurological; D009415:Nerve Net / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D010364:Pattern Recognition, Visual; D010775:Photic Stimulation; D013826:Theta Rhythm / Q000502:physiology*