I'm trying to gain some intuitions about how a given network structure would respond to dynamic inputs to one (or more) of it's nodes. Is there any way that the 'activity' within a particular node can be yoked to the output of a particular algorithm (e.g. a poisson process or the output of a iterated function)?
To create an activity generator go the network > insert menu and select activity generator. We don't have possoin but there are other distributions you can mess with. There is a logistic map which is a simple iterated function. And of course, you can make little circuits which are also basically iterated functions and send their outputs to another neuron.
No reading comes to mind. Neural networks are in a sense already iterated functions, albeit fairly complex ones. At every update a function is computed, and the results of that are fed back in to the system for the next update. So pressing the step button is, in a sense, iterating a function. But you are probably thinking more about simple systems like the logistic map. You could do something like train a backprop network to do the same thing as a logistic map, but there is no general readings on the topic I can think of. Cheers,