Recovery of Sequential and Non Sequential Memories with a Neural Mass Model

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 autoassociative memories for storage and recovery of multiple patterns can be built using biologically inspired models which simulate brain rhythms, and that the model which learns sequences can recover much more patterns.