Computational Differences Between
Asymmetrical and Symmetrical Networks
Zhaoping Li and Peter Dayan
Gatsby Computational Neuroscience Unit
University College London
Network: Computation in Neural Systems 10. 1 59-77, 1999
Abstract
Symmetrically connected recurrent networks have recently been used as
models of a host of neural computations. However, biological neural networks have
asymmetrical connections, at the very least because of the separation between excitatory
and inhibitory neurons in the brain. We study characteristic differences between
asymmetrical networks and their symmetrical counterparts in cases for which they act as
selective amplifiers for particular classes of input patterns. We show that the
dramatically different dynamical behaviours to which they have access, often make the
asymmetrical networks computationally superior. We illustrate our results in networks that
selectively amplify oriented bars and smooth contours in visual inputs.
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