robintibor
robintibor
Second vote: `n_in_chan[nel]s, n_out_chan[nel]s`  `n_chan[nels], n_classes`  `in_chan[nel]s, out_chan[nel]s` 
What a marvelous way to vote! @hubertjb @sliwy @gemeinl @agramfort notice second vote also up
keep in mind in the end n_classes loses meaning for regression tasks obviously
In the same pass as code style, we can also do naming stuff. There we have to decide about supercrop-language, if we want to keep it and where etc.
Another Point: * Expression -> Lambda?
I think one issues is we have multiple things which refer to timewindows and differ in the meaning of the timewindows: * Trials, as @hubertjb said, tied to a specific...
So my view atm: * Trial * ComputeWindow * ReceptiveField (possibly only needed in very few places) Then we use the established terms "trial" and "receptivefield", and have a new...
Thinking about it if we use ComputeCrop and this is only time we use crop, inside the code we could shorten to crop?
yes it is a single input to the network. the larger you make this input, the more predictions you will get -> the more are computed in parallel, saving you...
Unfortunately, "we have predictions for all receptive_fields and average them to obtain final window prediction" is not correct in general. For example, one use case that we had before and...