Computational Differences between Asymmetrical and Symmetrical Networks

Symmetrically connected recurrent networks have recently been used as models of a host of neural computations. However, be(cid: 173) cause of the separation between excitation and inhibition, biolog(cid: 173) ical neural networks are asymmetrical. We study characteristic differences between asymmetrical networks and their symmetri(cid: 173) cal 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.

Authors

• Zhaoping Li
• Peter Dayan

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