Rset or reboot neuron without exist in python

When Python is the interpreter, what is a good
design for the interface to the basic NEURON
concepts.

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anandhupresannan
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Rset or reboot neuron without exist in python

Post by anandhupresannan » Wed Feb 27, 2019 1:14 am

Hai,

I am working on a multi-compartmental model which is done in python with the help of neuron package. Currently the model is working. But I have a problem after the first run if I run the model again without existing the model, its showing different output. The model providing the correct output only in the first run. Is there any methods or functions which is available to reset or restart the neuron without existing.

Thank you

ted
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Re: Rset or reboot neuron without exist in python

Post by ted » Thu Feb 28, 2019 11:34 am

Something persists after the end of one simulation, and that something affects the next simulation. This happens when initialization is inadequate, so that, when you launch a second simulation, the value of one or more variables that were affected by the previous simulation are not first restored to what they were before the start of the first simulation. The affected variables might be among the biological properties of the model itself (e.g. a gating state, in which case a mod file with an incorrect INITIAL block would be a likely cause) or they could be variables that control stimuli applied to the model (for example, an index or "seed" that controls values generated by a pseudorandom number generator used to emulate current noise or stochastic variation of a gating state).

This is a serious problem that needs to be fixed before you try to do serious work with your model. If simulation results are to be useful for gaining insight to a system, there must be a close match between your conceptual model (your idea of what equations the computer is solving) and the computational model (the equations that the computer is actually solving). The first step in verifying such a match is demonstrating that a computational model generates exactly the same result from one run to the next. If it doesn't, you can't determine which result is the "correct" one; indeed, both runs may be incorrect. In none of these cases is it possible to establish that there is a close match between conceptual and computational model.

The way to proceed is to replace your existing model with a much simpler model that generates reproducible results. Then add complexities to it, one at a time, testing after each new complication to make sure that simulation results are reproducible.

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