Department of Electronics, Computer Sciences and Systems, University of Bologna, Via Venezia, 52, Cesena (FC), 47521, Italy.
Int J Neural Syst. 2013 Jun;23(3):1250036. doi: 10.1142/S0129065712500360. Epub 2013 Mar 26.
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.
提出了一种用于序列记忆的神经质量模型。它利用了生成θ/γ节律的三层皮质柱。第一层实现了在θ范围内的自联想记忆；第二层在γ范围内对物体进行分段；最后，第三层和第二层之间的反馈相互作用实现了异联想记忆，用于学习序列。在使用赫布和反赫布规则进行训练后，该网络恢复了序列并解释了相位超前现象。
