Hi,
I am trying to create a reconstructed model in neuron using a template. In the process there is a situation where I have to connect a branch in both the ends ie., 0 and 1.
connect treeA[me](0),treeA[parent](side)
connect treeA[me](1), treeA[parent1](side1)
I get the following Notice
Notice: SThcell[0].treeA[3](0) had previously been connected to parent SThcell[0].treeA[1](1)
The shape plot when viewed shows that the branch is connected only in one end. Am I missing something and is there a way to connect a branch on both ends to two others?
Ps: If information is insufficient I apologize for the same. Kindly let me know and I shall try explaining better my problem.
connect child, parent
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ted
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Re: connect child, parent
NEURON is doing exactly what it should, right down to the notice it printed.
A section can have an unlimited number of children, but only one parent.
Your code is attaching a section to one parent, then attaching it to a different
parent. Read about connect in the Programmer's Reference
http://www.neuron.yale.edu/neuron/stati ... ml#connect
If you're using MSWin, click on the Documentation item in the NEURON program
group and look up conect in the alphabetical index.
Everybody does this kind of thing at least once when they first start to build models
with hoc. In fact, one could reasonably argue that one's rate of learning is proportional
to the frequency with which one encounters new error messages.
So when somebody writes a letter of recommendation that says, among other things,
"so and so is a fast learner," do they really mean "don't let this one stand too close
to heavy machinery"?
A section can have an unlimited number of children, but only one parent.
Your code is attaching a section to one parent, then attaching it to a different
parent. Read about connect in the Programmer's Reference
http://www.neuron.yale.edu/neuron/stati ... ml#connect
If you're using MSWin, click on the Documentation item in the NEURON program
group and look up conect in the alphabetical index.
Everybody does this kind of thing at least once when they first start to build models
with hoc. In fact, one could reasonably argue that one's rate of learning is proportional
to the frequency with which one encounters new error messages.
So when somebody writes a letter of recommendation that says, among other things,
"so and so is a fast learner," do they really mean "don't let this one stand too close
to heavy machinery"?