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LETTER Communicated by Xiao-Jing Wang Design of Continuous Attractor Networks with Monotonic
 

Summary: LETTER Communicated by Xiao-Jing Wang
Design of Continuous Attractor Networks with Monotonic
Tuning Using a Symmetry Principle
Christian K. Machens
machens@zi.biologie.uni-muenchen.de
Carlos D. Brody
brody@princeton.edu
Cold Spring Harbor Laboratory, Cold Spring Harbor, NY 11724, U.S.A.
Neurons that sustain elevated firing in the absence of stimuli have been
found in many neural systems. In graded persistent activity, neurons can
sustain firing at many levels, suggesting a widely found type of network
dynamics in which networks can relax to any one of a continuum of sta-
tionary states. The reproduction of these findings in model networks of
nonlinear neurons has turned out to be nontrivial. A particularly insight-
ful model has been the "bump attractor," in which a continuous attractor
emerges through an underlying symmetry in the network connectivity
matrix. This model, however, cannot account for data in which the per-
sistent firing of neurons is a monotonic--rather than a bell-shaped--
function of a stored variable. Here, we show that the symmetry used in
the bump attractor network can be employed to create a whole family of

  

Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Machens, Christian - Group for Neural Theory, École Normale Supérieure, Paris

 

Collections: Biology and Medicine; Computer Technologies and Information Sciences