@article {IOPORT.05932170,
    author       = {Pascanu, Razvan and Jaeger, Herbert},
    title        = {A neurodynamical model for working memory.},
    year         = {2011},
    journal      = {Neural Networks},
    volume       = {24},
    number       = {2},
    issn         = {0893-6080},
    pages        = {199-207},
    publisher    = {Elsevier Science (Pergamon), Boston, MA},
    doi          = {10.1016/j.neunet.2010.10.003},
    abstract     = {Summary: Neurodynamical models of working memory (WM) should provide mechanisms for storing, maintaining, retrieving, and deleting information. Many models address only a subset of these aspects. Here we present a rather simple WM model in which all of these performance modes are trained into a recurrent neural network (RNN) of the echo state network (ESN) type. The model is demonstrated on a bracket level parsing task with a stream of rich and noisy graphical script input. In terms of nonlinear dynamics, memory states correspond, intuitively, to attractors in an input-driven system. As a supplementary contribution, the article proposes a rigorous formal framework to describe such attractors, generalizing from the standard definition of attractors in autonomous (input-free) dynamical systems.},
    identifier   = {05932170},
}