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Updated: May 11, 2026
Automatic Detection of Highly Organized Theta Oscillations in the Murine EEG
Published on: March 10, 2017
A multi-layer neural-mass model for learning sequences using theta/gamma oscillations
1Department of Electronics, Computer Sciences and Systems, University of Bologna, Via Venezia, 52, Cesena (FC), 47521, Italy. filippo.cona2@unibo.it
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.
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