Self-adaptively gated associative memory networks.

Synaptic firing in living organisms is modulated through molecules, i.e.

neuropeptides, that can communicate neurons many synapses away from

each other. Here we study the effects of multiplicative, neuropeptide-like

gating on associative memory retrieval using a gated RNN. Using

numerical simulations and dynamical mean field theory, we find that

gating enhances the memory capacity of a graded time continuous

Hopfield model and possibly gives rise to generalization by the appearance

of continuous attractors around the fixed-point patterns.