Computational differences between asymmetrical and symmetrical networks.
59 - 77.
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.
|Title:||Computational differences between asymmetrical and symmetrical networks|
|Open access status:||An open access version is available from UCL Discovery|
|Keywords:||VISUAL-CORTEX, NEURAL NETWORKS, OSCILLATORY RESPONSES, MATHEMATICAL-THEORY, MODEL, ORIENTATION, DYNAMICS, NEURONS, SELECTIVITY, PARALLEL|
|UCL classification:||UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
UCL > School of BEAMS > Faculty of Engineering Science > Computer Science
Archive Staff Only