I've done your typical ANN 101 training in the past so have a good mental model for back-propagation. Modelling the actual nonlinear dynamics of "realistic" neural networks seems like an obvious path of research but I know how daunting it is. It seems like every tiny bit we can push forward our tools for understanding complex non-linear systems should pay large dividends across so many different computational fields (fluid dynamics, QFT, economics, ..., everything?)
I'll have to read Izhikevich's paper, seems like a unique line of research.
Coincidence detection where delays are playing a functional role is one of the things that I find interesting (as well as the fact that there are more polychronous groups than neurons). The other thing is the emergence of gamma waves. I would be surprised if these do also not have some functional role (although it might be just as well the humming of our biological processor). :-)
I wish I was brave enough to start experimenting with different neural networks. For now I am on the roll "Bayesianfying" everything I encounter. Even the Hough transform that Hinton is so fond of in this talk. :-)
I've done your typical ANN 101 training in the past so have a good mental model for back-propagation. Modelling the actual nonlinear dynamics of "realistic" neural networks seems like an obvious path of research but I know how daunting it is. It seems like every tiny bit we can push forward our tools for understanding complex non-linear systems should pay large dividends across so many different computational fields (fluid dynamics, QFT, economics, ..., everything?)
I'll have to read Izhikevich's paper, seems like a unique line of research.