Methods - Model
We introduce a model implementation using Python and Numpy in order to simulate the aforementioned coupled ODEs. The important entities of the model are represented by their own classes, namely multiple ORNs as well as a single output neuron. A specific scent class allows us to spatially and temporally describe scent progressions, which assigns a 1-dimensional scent progression to every ORN. The same goes for a Tau progression per ORN.
This flexible framework allows us to conduct multiple experiments without any effort, whether it is a change of antenna length, adding another ORN or making a change in the temporal progression of the odor we want to simulate.
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