Title:
Computational differences between asymmetrical and symmetrical networks
Authors: Zhaoping Li and Peter Dayan
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.
To appear in : Network: Computation in Neural Systems 10. 1 59-77, 1999.