Black-box modeling with Neuron

[sleepmode]

Hi all,

apologies if this question sounds pretty stupid but I am new to Neuron and after looking into it for a week or so, I have to make sure.

So, we are trying to model the behavior of specific neuron clusters in the brain which are a "black box" to us.
What we have is very comprehensive input/output data based on animal experiments.
What we want is to come up with a functional model of those black boxes based on the input/output data that we are feeding them.
Thus, to sort of _extrapolate_ the behavior of a neuron network based on its response to various input stimuli.

Can this be done with the Neuron language?

Thank you for your time.

Kind regards,
Chris

[ted]

It's doable because NEURON has two "complete" programming languages (hoc and Python),
plus sophisticated event delivery and runtime systems that can be used to implement
state machines and complex simulation flow control. However, most of NEURON's
special strengths have to do with specifying models in which mechanism is important,
or in which the operation of individual neurons can be reasonably approximated by
artificial spiking cells.

So what you describe is not really NEURON's forte. Best for your purpose would be a
simulation environment that allows you to set up functional blocks whose input-output
relationships can be specified in ways that are easy for you to map your conceptual model
onto. I wish I had a particular recommendation for you.