Lab Home | Phone | Search | ||||||||
|
||||||||
A number of simple learning rules have been proposed and implemented in artificial neural networks. To date, most attempts to understand the long-term dynamics of these rules have been based around simulations and experiments. An alternative approach is to try and understand the behavior of these rules from an analytical standpoint. Here we attempt to do so for a few simple cases using a Fokker-Planck formalism, and show that for some rules, the analysis yields meaningful results, while for others the approach breaks down or becomes very difficult. Despite these difficulties we are still able to highlight basic differences between learning rules. Furthermore we can identify several potential avenues by which this research can be extended in the future. Host: Garrett Kenyon |