  We extend a recently introduced class of exactly solvable models for
recurrent neural networks with competition between 1D nearest neighbour and
infinite range information processing. We increase the potential for further
frustration and competition in these models, as well as their biological
relevance, by adding next-nearest neighbour couplings, and we allow for
modulation of the attractors so that we can interpolate continuously between
situations with different numbers of stored patterns. Our models are solved by
combining mean field and random field techniques. They exhibit increasingly
complex phase diagrams with novel phases, separated by multiple first- and
second order transitions (dynamical and thermodynamic ones), and, upon
modulating the attractor strengths, non-trivial scenarios of phase diagram
deformation. Our predictions are in excellent agreement with numerical
simulations.
