> > Nonlinear dendritic integration can be accurately captured by the > comparmental model which divides dendrites into small sections > with ion channels and other internal reaction mechanisms. This > is the most accurate level of modeling. It may be possible to > simplify this model with machine learning techniques and without > significant loss in accuracy.
I am well aware of compartmental modelling and have done it myself. But this type of model only accounts for the physical size/character of a dendrite, ignoring, in principle, a whole raft of complex molecular dynamics of what might be occuring inside it. Such molecular dynamics will sure contribute to the nonlinear aspects of a dendrite. > > >Just as an example, a new type of neuron has recently been discovered that > >can hold a steady state of firing in isolation, apply current, rate > >increases and remains stable at a new threshold. It's dynamically > >settable, which blows away all standard Integrate & Fire models. > > I don't know the exact mechanisms that give rise to that type > of neurons, but the comparmental model should be able to cover > this. What is needed is a large-scale database of neuronal > characteristics (automation). Yes, one can create a model of a neuron that does this, it's already been done. It's far from a standard model though. My point, however, was that there is an entire world of complexity within the cell that will be relevant to its role in a neural network (as opposed to simply metabolic) that we are just beginning to understanding. ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/[EMAIL PROTECTED]
