Neural networks are a (currently not very successful) attempt to programmatically mimic the learning behaviour of a human brain. Research in neural networks has mostly focused on topologies and transfer functions in the nodes. However, the aspect of time has been neglected. Human nerves transport signals with (relatively slow) speed between 0.5 and 120 m/s (quoting a random reference from the web).
Could it be that our learning capability depends not only on particular signal values (the part that artificial neural networks are simulating), but also on propagation time between neurons in the brain? A signal might have different effect on a neuron in the brain, depending on the time of signal's arrival.
This would add another dimension to artificial neural networks: temporally changing transfer functions in the nodes. This is just an idea for further research, maybe someone has already looked into it.