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.
A Multi-Layer Neural-Mass Model for Learning Sequences Using Theta/Gamma Oscillations / Filippo Cona; Mauro Ursino. - In: INTERNATIONAL JOURNAL OF NEURAL SYSTEMS. - ISSN 0129-0657. - ELETTRONICO. - 23:3(2013), pp. 1-18. [10.1142/S0129065712500360]
A Multi-Layer Neural-Mass Model for Learning Sequences Using Theta/Gamma Oscillations
CONA, FILIPPO;URSINO, MAURO
2013
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.