Dear Nathan, Your description of the kind of neural-net scheme needs more detail before I can give any more particular direction. It leaves a huge amount of possibilities. For example, is your neural net similar to hierarchical network of abstract facts, being build by agents, whereas the agent's own functionality (parameters) being altered by a (re)engineering agent guided by expectation maximization over the training samples? Or is it more like a classical neural network, being the difference that your neurons perform a more exotic function of their input? I've been thinking about 'evolving neural networks that are good at learning spatial and temporal patterns' as well, but I have to say the theoretical possibilities are endless, but scientifically vapour, if not tested on some *real* data. To be taken seriously, you need a good specification of your problem, and a straigth-forward way is to select some representative training set, preferably used by previously published papers.
Diederik Kingma On 12/26/06, Nathan Cook <[EMAIL PROTECTED]> wrote:
Hello list, I have an idea for evolving neural networks that are good at learning spatial and temporal patterns. However, to implement it, I need a model for a spiking neural net, such that each neuron has several different parameters affecting its operation (I will then use genetic algorithms to modify these parameters). The richer the behaviour of the neurons the better. Can someone direct me to a suitable resource? I would be pleased to provide more details if anyone's interested - my goal is to create networks which can learn more than one sort of information (say, visual and audio) at once, and even do some form of induction on this information. Nathan Cook ------------------------------ This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
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