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A unified approach to building and controlling spiking attractor networks

Summary: NC ms 3009
A unified approach to building and
controlling spiking attractor networks
Chris Eliasmith
23rd September 2004
Extending work in Eliasmith and Anderson (2003), I employ a general frame-
work to construct biologically plausible simulations of the three classes of attrac-
tor networks relevant for biological systems: static (point, line, ring, and plane)
attractors; cyclic attractors; and chaotic attractors. I discuss these attractors in the
context of the neural systems that they have been posited to help explain: eye con-
trol, working memory, and head direction; locomotion (specifically swimming);
and olfaction, respectively. I then demonstrate how to introduce control into these
models. The addition of control shows how attractor networks can be used as
subsystems in larger neural systems, demonstrates how a much larger class of
networks can be related to attractor networks, and makes it clear how attractor
networks can be exploited for various information processing tasks in neurobio-
logical systems.
1 Introduction
Persistent activity has been thought to be important for neural computation at least since


Source: Anderson, Charles H. - Departments of Anatomy and Neurobiology & Physics, Washington University in St. Louis


Collections: Computer Technologies and Information Sciences; Biology and Medicine