A multi-layer neural-mass model for learning sequences using theta/gamma oscillations.
作者： FILIPPO CONA , MAURO URSINO
DOI: 10.1142/S0129065712500360
关键词:
摘要: A neural mass model for the memorization of sequences is presented. It exploits three layers cortical columns that generate a theta/gamma rhythm. The first layer implements an auto-associative memory working in theta range; second segments objects gamma finally, feedback interactions between third and realize hetero-associative learning sequence. After training with Hebbian anti-Hebbian rules, network recovers accounts phase-precession phenomenon.
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