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Abstract:
Symmetrically connected recurrent networks have recently been
used as models of a host of neural computations. However, because
of the separation between excitation and inhibition, biological
neural networks are asymmetrical. We study characteristic
differences between asymmetrical networks and their symmetrical
counterparts, showing that they have dramatically different
dynamical behavior and also how the differences can be exploited
for computational ends. We illustrate our results in the case of a
network that is a selective amplifier.
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