Continuous attractors and oculomotor control

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Neural Turing Machines (2014, background): “LSTM ameliorates the problem by embedding perfect integrators (Seung, 1998) for memory storage in the network.”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “Neural models of working memory can store not only scalar variables (Seung, 1998), but also high-dimensional vectors (Eliasmith and Anderson, 2004; Eliasmith et al., 2012) or sequences of vectors (Choo and Eliasmith, 2010).”
Flexible Kernel Memory (2010, background): “…demonstrated persistent activity in several structures in the lower mammal, primate, and human brains including the hippocampus [1,2], prefrontal [3], visual [4] and oculomotor cortex [5], basal ganglia [6], etc. Persistent dynamics is believed to emerge as attractor dynamics (see also [7]–[13]).”
On partial contraction analysis for coupled nonlinear oscillators (2004, background): “Then i is contracting (with Example 2.3 Recent research in computational neuroscience points out the importance of continuous attractors (Latham et al. 2003; Seung 1998).”
Interactions between frontal cortex and basal ganglia in working memory: A computational model (2001, background): “Finally, there are a number of other computational models that focus mainly on the PFC without a detailed consideration of the role of the basal ganglia (Dehaene & Changeux, 1989, 1991; Guigon, Dorizzi, Burnod, & Schultz, 1995; Moody et al., 1998; Seung, 1998; Tanaka & Okada, 1999; Zipser, 1991).”