Comments (5)
That sounds like a good idea. I guess, for many models that have been around for a while, people have already used transfer functions to produce firing rate output, which could then simply be used. This would also match the behaviour of the model in neurolib with the behaviour of the same model outside of neurolib. For example, someone reads papers about model A (for which transfer function f_A is traditionally being used) and wants to use it in neurolib. However, they are not aware of the different output scaling in neurolib right now. This could cause confusion and even wrong conclusions. The proposed change would prevent that.
from neurolib.
+1 for the user-defined lambda
functions. for identity (i.e. no transform), it can be just lambda x: x
from neurolib.
People have already used transfer functions to produce firing rate output ... However, they are not aware of the different output scaling in neurolib right now
Right now, the output itself is not scaled before being "recorded" to the model.output
object. This could be done in a very similar way but also done by the model integration itself. aln
for example is originally implemented to output kHz
but I convert it to Hz
in a very hackish way (see Issue #53 ...). I don't quite like it I must admit because the aln
model also expects kHz
input. So once you start transforming outputs, I think you might also have to start transforming inputs...
from neurolib.
Thanks for the input from both of you. I will make a PR on this, stay tuned!
from neurolib.
Closed by #59
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Related Issues (20)
- Citation HOT 1
- Better error catching in exploration
- There are 0 valid individuals Mean score across population: nan HOT 7
- If set explicitly, local connectivity weights for ALNNode change after running the model HOT 3
- Try to make MultiModel faster HOT 1
- Neurolib satellite tutorial session at CNS*2022 HOT 20
- Testing: test_models with dynamic loading HOT 1
- TypeError: <class 'numpy.typing._dtype_like._SupportsDType'> is not a generic class HOT 3
- Help Using Neurolib: Finding an easy way to load new data for the model implementation HOT 1
- Question about the animation HOT 1
- Parameter exploration bug: PicklingError HOT 5
- Cmat is the same in two model instances, but models themselves are not HOT 1
- WW model is missing in example and test
- Documentation pipeline fails HOT 1
- model parameters and stimulation
- Parameters provided in examples (and default parameters) does not perform BOLD simulation correctly HOT 1
- Possible deprecation issues in the example file of multimodel_custom_model HOT 1
- Example files appear to be broken
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