A Neural Mass Model for the Recovery of Memorized Sequences

A neural model for the recovery of learnt patterns is presented. The model simulates the theta-gamma activity associated to memory recall. Two versions of the model are described: the first can learn generic patterns without a given order, while the second learns patterns in a specific sequence. The latter has been implemented to overcome the limited recovery capacity of the former. The network is trained using Hebbian and anti-Hebbian paradigms, and exploits excitatory and inhibitory mutual synapses. The results show that the model which learns sequences can recover much more patterns within a single theta cycle.